In [ ]:
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<title>Impact_of_Employment_housingprice (4)</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
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/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
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* Copyright (c) Jupyter Development Team.
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/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
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| The full license is in the file LICENSE, distributed with this software.
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* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
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/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
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/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
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* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
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/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
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* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
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/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
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/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
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* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
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/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
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| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
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/* Icons urls */
:root {
--jp-icon-add-above: url(data:image/svg+xml;base64,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);
--jp-icon-add-below: url(data:image/svg+xml;base64,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);
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bell: url(data:image/svg+xml;base64,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);
--jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
--jp-icon-bug: url(data:image/svg+xml;base64,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);
--jp-icon-build: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE0LjkgMTcuNDVDMTYuMjUgMTcuNDUgMTcuMzUgMTYuMzUgMTcuMzUgMTVDMTcuMzUgMTMuNjUgMTYuMjUgMTIuNTUgMTQuOSAxMi41NUMxMy41NCAxMi41NSAxMi40NSAxMy42NSAxMi40NSAxNUMxMi40NSAxNi4zNSAxMy41NCAxNy40NSAxNC45IDE3LjQ1Wk0yMC4xIDE1LjY4TDIxLjU4IDE2Ljg0QzIxLjcxIDE2Ljk1IDIxLjc1IDE3LjEzIDIxLjY2IDE3LjI5TDIwLjI2IDE5LjcxQzIwLjE3IDE5Ljg2IDIwIDE5LjkyIDE5LjgzIDE5Ljg2TDE4LjA5IDE5LjE2QzE3LjczIDE5LjQ0IDE3LjMzIDE5LjY3IDE2LjkxIDE5Ljg1TDE2LjY0IDIxLjdDMTYuNjIgMjEuODcgMTYuNDcgMjIgMTYuMyAyMkgxMy41QzEzLjMyIDIyIDEzLjE4IDIxLjg3IDEzLjE1IDIxLjdMMTIuODkgMTkuODVDMTIuNDYgMTkuNjcgMTIuMDcgMTkuNDQgMTEuNzEgMTkuMTZMOS45NjAwMiAxOS44NkM5LjgxMDAyIDE5LjkyIDkuNjIwMDIgMTkuODYgOS41NDAwMiAxOS43MUw4LjE0MDAyIDE3LjI5QzguMDUwMDIgMTcuMTMgOC4wOTAwMiAxNi45NSA4LjIyMDAyIDE2Ljg0TDkuNzAwMDIgMTUuNjhMOS42NTAwMSAxNUw5LjcwMDAyIDE0LjMxTDguMjIwMDIgMTMuMTZDOC4wOTAwMiAxMy4wNSA4LjA1MDAyIDEyLjg2IDguMTQwMDIgMTIuNzFMOS41NDAwMiAxMC4yOUM5LjYyMDAyIDEwLjEzIDkuODEwMDIgMTAuMDcgOS45NjAwMiAxMC4xM0wxMS43MSAxMC44NEMxMi4wNyAxMC41NiAxMi40NiAxMC4zMiAxMi44OSAxMC4xNUwxMy4xNSA4LjI4OTk4QzEzLjE4IDguMTI5OTggMTMuMzIgNy45OTk5OCAxMy41IDcuOTk5OThIMTYuM0MxNi40NyA3Ljk5OTk4IDE2LjYyIDguMTI5OTggMTYuNjQgOC4yODk5OEwxNi45MSAxMC4xNUMxNy4zMyAxMC4zMiAxNy43MyAxMC41NiAxOC4wOSAxMC44NEwxOS44MyAxMC4xM0MyMCAxMC4wNyAyMC4xNyAxMC4xMyAyMC4yNiAxMC4yOUwyMS42NiAxMi43MUMyMS43NSAxMi44NiAyMS43MSAxMy4wNSAyMS41OCAxMy4xNkwyMC4xIDE0LjMxTDIwLjE1IDE1TDIwLjEgMTUuNjhaIi8+CiAgICA8cGF0aCBkPSJNNy4zMjk2NiA3LjQ0NDU0QzguMDgzMSA3LjAwOTU0IDguMzM5MzIgNi4wNTMzMiA3LjkwNDMyIDUuMjk5ODhDNy40NjkzMiA0LjU0NjQzIDYuNTA4MSA0LjI4MTU2IDUuNzU0NjYgNC43MTY1NkM1LjM5MTc2IDQuOTI2MDggNS4xMjY5NSA1LjI3MTE4IDUuMDE4NDkgNS42NzU5NEM0LjkxMDA0IDYuMDgwNzEgNC45NjY4MiA2LjUxMTk4IDUuMTc2MzQgNi44NzQ4OEM1LjYxMTM0IDcuNjI4MzIgNi41NzYyMiA3Ljg3OTU0IDcuMzI5NjYgNy40NDQ1NFpNOS42NTcxOCA0Ljc5NTkzTDEwLjg2NzIgNC45NTE3OUMxMC45NjI4IDQuOTc3NDEgMTEuMDQwMiA1LjA3MTMzIDExLjAzODIgNS4xODc5M0wxMS4wMzg4IDYuOTg4OTNDMTEuMDQ1NSA3LjEwMDU0IDEwLjk2MTYgNy4xOTUxOCAxMC44NTUgNy4yMTA1NEw5LjY2MDAxIDcuMzgwODNMOS4yMzkxNSA4LjEzMTg4TDkuNjY5NjEgOS4yNTc0NUM5LjcwNzI5IDkuMzYyNzEgOS42NjkzNCA5LjQ3Njk5IDkuNTc0MDggOS41MzE5OUw4LjAxNTIzIDEwLjQzMkM3LjkxMTMxIDEwLjQ5MiA3Ljc5MzM3IDEwLjQ2NzcgNy43MjEwNSAxMC4zODI0TDYuOTg3NDggOS40MzE4OEw2LjEwOTMxIDkuNDMwODNMNS4zNDcwNCAxMC4zOTA1QzUuMjg5MDkgMTAuNDcwMiA1LjE3MzgzIDEwLjQ5MDUgNS4wNzE4NyAxMC40MzM5TDMuNTEyNDUgOS41MzI5M0MzLjQxMDQ5IDkuNDc2MzMgMy4zNzY0NyA5LjM1NzQxIDMuNDEwNzUgOS4yNTY3OUwzLjg2MzQ3IDguMTQwOTNMMy42MTc0OSA3Ljc3NDg4TDMuNDIzNDcgNy4zNzg4M0wyLjIzMDc1IDcuMjEyOTdDMi4xMjY0NyA3LjE5MjM1IDIuMDQwNDkgNy4xMDM0MiAyLjA0MjQ1IDYuOTg2ODJMMi4wNDE4NyA1LjE4NTgyQzIuMDQzODMgNS4wNjkyMiAyLjExOTA5IDQuOTc5NTggMi4yMTcwNCA0Ljk2OTIyTDMuNDIwNjUgNC43OTM5M0wzLjg2NzQ5IDQuMDI3ODhMMy40MTEwNSAyLjkxNzMxQzMuMzczMzcgMi44MTIwNCAzLjQxMTMxIDIuNjk3NzYgMy41MTUyMyAyLjYzNzc2TDUuMDc0MDggMS43Mzc3NkM1LjE2OTM0IDEuNjgyNzYgNS4yODcyOSAxLjcwNzA0IDUuMzU5NjEgMS43OTIzMUw2LjExOTE1IDIuNzI3ODhMNi45ODAwMSAyLjczODkzTDcuNzI0OTYgMS43ODkyMkM3Ljc5MTU2IDEuNzA0NTggNy45MTU0OCAxLjY3OTIyIDguMDA4NzkgMS43NDA4Mkw5LjU2ODIxIDIuNjQxODJDOS42NzAxNyAyLjY5ODQyIDkuNzEyODUgMi44MTIzNCA5LjY4NzIzIDIuOTA3OTdMOS4yMTcxOCA0LjAzMzgzTDkuNDYzMTYgNC4zOTk4OEw5LjY1NzE4IDQuNzk1OTNaIi8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
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--jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-regex: url(data:image/svg+xml;base64,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);
--jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-toc: url(data:image/svg+xml;base64,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);
--jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
--jp-icon-trusted: url(data:image/svg+xml;base64,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);
--jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-users: url(data:image/svg+xml;base64,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);
--jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-word: url(data:image/svg+xml;base64,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);
--jp-icon-yaml: url(data:image/svg+xml;base64,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);
}
/* Icon CSS class declarations */
.jp-AddAboveIcon {
background-image: var(--jp-icon-add-above);
}
.jp-AddBelowIcon {
background-image: var(--jp-icon-add-below);
}
.jp-AddIcon {
background-image: var(--jp-icon-add);
}
.jp-BellIcon {
background-image: var(--jp-icon-bell);
}
.jp-BugDotIcon {
background-image: var(--jp-icon-bug-dot);
}
.jp-BugIcon {
background-image: var(--jp-icon-bug);
}
.jp-BuildIcon {
background-image: var(--jp-icon-build);
}
.jp-CaretDownEmptyIcon {
background-image: var(--jp-icon-caret-down-empty);
}
.jp-CaretDownEmptyThinIcon {
background-image: var(--jp-icon-caret-down-empty-thin);
}
.jp-CaretDownIcon {
background-image: var(--jp-icon-caret-down);
}
.jp-CaretLeftIcon {
background-image: var(--jp-icon-caret-left);
}
.jp-CaretRightIcon {
background-image: var(--jp-icon-caret-right);
}
.jp-CaretUpEmptyThinIcon {
background-image: var(--jp-icon-caret-up-empty-thin);
}
.jp-CaretUpIcon {
background-image: var(--jp-icon-caret-up);
}
.jp-CaseSensitiveIcon {
background-image: var(--jp-icon-case-sensitive);
}
.jp-CheckIcon {
background-image: var(--jp-icon-check);
}
.jp-CircleEmptyIcon {
background-image: var(--jp-icon-circle-empty);
}
.jp-CircleIcon {
background-image: var(--jp-icon-circle);
}
.jp-ClearIcon {
background-image: var(--jp-icon-clear);
}
.jp-CloseIcon {
background-image: var(--jp-icon-close);
}
.jp-CodeCheckIcon {
background-image: var(--jp-icon-code-check);
}
.jp-CodeIcon {
background-image: var(--jp-icon-code);
}
.jp-CollapseAllIcon {
background-image: var(--jp-icon-collapse-all);
}
.jp-ConsoleIcon {
background-image: var(--jp-icon-console);
}
.jp-CopyIcon {
background-image: var(--jp-icon-copy);
}
.jp-CopyrightIcon {
background-image: var(--jp-icon-copyright);
}
.jp-CutIcon {
background-image: var(--jp-icon-cut);
}
.jp-DeleteIcon {
background-image: var(--jp-icon-delete);
}
.jp-DownloadIcon {
background-image: var(--jp-icon-download);
}
.jp-DuplicateIcon {
background-image: var(--jp-icon-duplicate);
}
.jp-EditIcon {
background-image: var(--jp-icon-edit);
}
.jp-EllipsesIcon {
background-image: var(--jp-icon-ellipses);
}
.jp-ErrorIcon {
background-image: var(--jp-icon-error);
}
.jp-ExpandAllIcon {
background-image: var(--jp-icon-expand-all);
}
.jp-ExtensionIcon {
background-image: var(--jp-icon-extension);
}
.jp-FastForwardIcon {
background-image: var(--jp-icon-fast-forward);
}
.jp-FileIcon {
background-image: var(--jp-icon-file);
}
.jp-FileUploadIcon {
background-image: var(--jp-icon-file-upload);
}
.jp-FilterDotIcon {
background-image: var(--jp-icon-filter-dot);
}
.jp-FilterIcon {
background-image: var(--jp-icon-filter);
}
.jp-FilterListIcon {
background-image: var(--jp-icon-filter-list);
}
.jp-FolderFavoriteIcon {
background-image: var(--jp-icon-folder-favorite);
}
.jp-FolderIcon {
background-image: var(--jp-icon-folder);
}
.jp-HomeIcon {
background-image: var(--jp-icon-home);
}
.jp-Html5Icon {
background-image: var(--jp-icon-html5);
}
.jp-ImageIcon {
background-image: var(--jp-icon-image);
}
.jp-InfoIcon {
background-image: var(--jp-icon-info);
}
.jp-InspectorIcon {
background-image: var(--jp-icon-inspector);
}
.jp-JsonIcon {
background-image: var(--jp-icon-json);
}
.jp-JuliaIcon {
background-image: var(--jp-icon-julia);
}
.jp-JupyterFaviconIcon {
background-image: var(--jp-icon-jupyter-favicon);
}
.jp-JupyterIcon {
background-image: var(--jp-icon-jupyter);
}
.jp-JupyterlabWordmarkIcon {
background-image: var(--jp-icon-jupyterlab-wordmark);
}
.jp-KernelIcon {
background-image: var(--jp-icon-kernel);
}
.jp-KeyboardIcon {
background-image: var(--jp-icon-keyboard);
}
.jp-LaunchIcon {
background-image: var(--jp-icon-launch);
}
.jp-LauncherIcon {
background-image: var(--jp-icon-launcher);
}
.jp-LineFormIcon {
background-image: var(--jp-icon-line-form);
}
.jp-LinkIcon {
background-image: var(--jp-icon-link);
}
.jp-ListIcon {
background-image: var(--jp-icon-list);
}
.jp-MarkdownIcon {
background-image: var(--jp-icon-markdown);
}
.jp-MoveDownIcon {
background-image: var(--jp-icon-move-down);
}
.jp-MoveUpIcon {
background-image: var(--jp-icon-move-up);
}
.jp-NewFolderIcon {
background-image: var(--jp-icon-new-folder);
}
.jp-NotTrustedIcon {
background-image: var(--jp-icon-not-trusted);
}
.jp-NotebookIcon {
background-image: var(--jp-icon-notebook);
}
.jp-NumberingIcon {
background-image: var(--jp-icon-numbering);
}
.jp-OfflineBoltIcon {
background-image: var(--jp-icon-offline-bolt);
}
.jp-PaletteIcon {
background-image: var(--jp-icon-palette);
}
.jp-PasteIcon {
background-image: var(--jp-icon-paste);
}
.jp-PdfIcon {
background-image: var(--jp-icon-pdf);
}
.jp-PythonIcon {
background-image: var(--jp-icon-python);
}
.jp-RKernelIcon {
background-image: var(--jp-icon-r-kernel);
}
.jp-ReactIcon {
background-image: var(--jp-icon-react);
}
.jp-RedoIcon {
background-image: var(--jp-icon-redo);
}
.jp-RefreshIcon {
background-image: var(--jp-icon-refresh);
}
.jp-RegexIcon {
background-image: var(--jp-icon-regex);
}
.jp-RunIcon {
background-image: var(--jp-icon-run);
}
.jp-RunningIcon {
background-image: var(--jp-icon-running);
}
.jp-SaveIcon {
background-image: var(--jp-icon-save);
}
.jp-SearchIcon {
background-image: var(--jp-icon-search);
}
.jp-SettingsIcon {
background-image: var(--jp-icon-settings);
}
.jp-ShareIcon {
background-image: var(--jp-icon-share);
}
.jp-SpreadsheetIcon {
background-image: var(--jp-icon-spreadsheet);
}
.jp-StopIcon {
background-image: var(--jp-icon-stop);
}
.jp-TabIcon {
background-image: var(--jp-icon-tab);
}
.jp-TableRowsIcon {
background-image: var(--jp-icon-table-rows);
}
.jp-TagIcon {
background-image: var(--jp-icon-tag);
}
.jp-TerminalIcon {
background-image: var(--jp-icon-terminal);
}
.jp-TextEditorIcon {
background-image: var(--jp-icon-text-editor);
}
.jp-TocIcon {
background-image: var(--jp-icon-toc);
}
.jp-TreeViewIcon {
background-image: var(--jp-icon-tree-view);
}
.jp-TrustedIcon {
background-image: var(--jp-icon-trusted);
}
.jp-UndoIcon {
background-image: var(--jp-icon-undo);
}
.jp-UserIcon {
background-image: var(--jp-icon-user);
}
.jp-UsersIcon {
background-image: var(--jp-icon-users);
}
.jp-VegaIcon {
background-image: var(--jp-icon-vega);
}
.jp-WordIcon {
background-image: var(--jp-icon-word);
}
.jp-YamlIcon {
background-image: var(--jp-icon-yaml);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* (DEPRECATED) Support for consuming icons as CSS background images
*/
.jp-Icon,
.jp-MaterialIcon {
background-position: center;
background-repeat: no-repeat;
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-cover {
background-position: center;
background-repeat: no-repeat;
background-size: cover;
}
/**
* (DEPRECATED) Support for specific CSS icon sizes
*/
.jp-Icon-16 {
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-18 {
background-size: 18px;
min-width: 18px;
min-height: 18px;
}
.jp-Icon-20 {
background-size: 20px;
min-width: 20px;
min-height: 20px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.lm-TabBar .lm-TabBar-addButton {
align-items: center;
display: flex;
padding: 4px;
padding-bottom: 5px;
margin-right: 1px;
background-color: var(--jp-layout-color2);
}
.lm-TabBar .lm-TabBar-addButton:hover {
background-color: var(--jp-layout-color1);
}
.lm-DockPanel-tabBar .lm-TabBar-tab {
width: var(--jp-private-horizontal-tab-width);
}
.lm-DockPanel-tabBar .lm-TabBar-content {
flex: unset;
}
.lm-DockPanel-tabBar[data-orientation='horizontal'] {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for icons as inline SVG HTMLElements
*/
/* recolor the primary elements of an icon */
.jp-icon0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-accent0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-accent1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-accent2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-accent3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-accent4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-accent0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-accent1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-accent2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-accent3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-accent4[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-none[fill] {
fill: none;
}
.jp-icon-none[stroke] {
stroke: none;
}
/* brand icon colors. Same for light and dark */
.jp-icon-brand0[fill] {
fill: var(--jp-brand-color0);
}
.jp-icon-brand1[fill] {
fill: var(--jp-brand-color1);
}
.jp-icon-brand2[fill] {
fill: var(--jp-brand-color2);
}
.jp-icon-brand3[fill] {
fill: var(--jp-brand-color3);
}
.jp-icon-brand4[fill] {
fill: var(--jp-brand-color4);
}
.jp-icon-brand0[stroke] {
stroke: var(--jp-brand-color0);
}
.jp-icon-brand1[stroke] {
stroke: var(--jp-brand-color1);
}
.jp-icon-brand2[stroke] {
stroke: var(--jp-brand-color2);
}
.jp-icon-brand3[stroke] {
stroke: var(--jp-brand-color3);
}
.jp-icon-brand4[stroke] {
stroke: var(--jp-brand-color4);
}
/* warn icon colors. Same for light and dark */
.jp-icon-warn0[fill] {
fill: var(--jp-warn-color0);
}
.jp-icon-warn1[fill] {
fill: var(--jp-warn-color1);
}
.jp-icon-warn2[fill] {
fill: var(--jp-warn-color2);
}
.jp-icon-warn3[fill] {
fill: var(--jp-warn-color3);
}
.jp-icon-warn0[stroke] {
stroke: var(--jp-warn-color0);
}
.jp-icon-warn1[stroke] {
stroke: var(--jp-warn-color1);
}
.jp-icon-warn2[stroke] {
stroke: var(--jp-warn-color2);
}
.jp-icon-warn3[stroke] {
stroke: var(--jp-warn-color3);
}
/* icon colors that contrast well with each other and most backgrounds */
.jp-icon-contrast0[fill] {
fill: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[fill] {
fill: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[fill] {
fill: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[fill] {
fill: var(--jp-icon-contrast-color3);
}
.jp-icon-contrast0[stroke] {
stroke: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[stroke] {
stroke: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[stroke] {
stroke: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[stroke] {
stroke: var(--jp-icon-contrast-color3);
}
.jp-icon-dot[fill] {
fill: var(--jp-warn-color0);
}
.jp-jupyter-icon-color[fill] {
fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
}
.jp-notebook-icon-color[fill] {
fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
}
.jp-json-icon-color[fill] {
fill: var(--jp-json-icon-color, var(--jp-warn-color1));
}
.jp-console-icon-color[fill] {
fill: var(--jp-console-icon-color, white);
}
.jp-console-icon-background-color[fill] {
fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
}
.jp-terminal-icon-color[fill] {
fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
}
.jp-terminal-icon-background-color[fill] {
fill: var(
--jp-terminal-icon-background-color,
var(--jp-inverse-layout-color2)
);
}
.jp-text-editor-icon-color[fill] {
fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
}
.jp-inspector-icon-color[fill] {
fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
}
/* CSS for icons in selected filebrowser listing items */
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* stylelint-disable selector-max-class, selector-max-compound-selectors */
/**
* TODO: come up with non css-hack solution for showing the busy icon on top
* of the close icon
* CSS for complex behavior of close icon of tabs in the main area tabbar
*/
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon3[fill] {
fill: none;
}
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon-busy[fill] {
fill: var(--jp-inverse-layout-color3);
}
/* stylelint-enable selector-max-class, selector-max-compound-selectors */
/* CSS for icons in status bar */
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* special handling for splash icon CSS. While the theme CSS reloads during
splash, the splash icon can loose theming. To prevent that, we set a
default for its color variable */
:root {
--jp-warn-color0: var(--md-orange-700);
}
/* not sure what to do with this one, used in filebrowser listing */
.jp-DragIcon {
margin-right: 4px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for alt colors for icons as inline SVG HTMLElements
*/
/* alt recolor the primary elements of an icon */
.jp-icon-alt .jp-icon0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-alt .jp-icon0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[stroke] {
stroke: var(--jp-layout-color4);
}
/* alt recolor the accent elements of an icon */
.jp-icon-alt .jp-icon-accent0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-alt .jp-icon-accent0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
display: none !important;
}
/**
* Support for hover colors for icons as inline SVG HTMLElements
*/
/**
* regular colors
*/
/* recolor the primary elements of an icon */
.jp-icon-hover :hover .jp-icon0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-hover :hover .jp-icon-none-hover[fill] {
fill: none;
}
.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
stroke: none;
}
/**
* inverse colors
*/
/* inverse recolor the primary elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* inverse recolor the accent elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-IFrame {
width: 100%;
height: 100%;
}
.jp-IFrame > iframe {
border: none;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-IFrame {
position: relative;
}
body.lm-mod-override-cursor .jp-IFrame::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-HoverBox {
position: fixed;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FormGroup-content fieldset {
border: none;
padding: 0;
min-width: 0;
width: 100%;
}
/* stylelint-disable selector-max-type */
.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
font-size: var(--jp-content-font-size2);
border-color: var(--jp-input-border-color);
border-style: solid;
border-radius: var(--jp-border-radius);
border-width: 1px;
padding: 6px 8px;
background: none;
color: var(--jp-ui-font-color0);
height: inherit;
}
.jp-FormGroup-content fieldset input[type='checkbox'] {
position: relative;
top: 2px;
margin-left: 0;
}
.jp-FormGroup-content button.jp-mod-styled {
cursor: pointer;
}
.jp-FormGroup-content .checkbox label {
cursor: pointer;
font-size: var(--jp-content-font-size1);
}
.jp-FormGroup-content .jp-root > fieldset > legend {
display: none;
}
.jp-FormGroup-content .jp-root > fieldset > p {
display: none;
}
/** copy of `input.jp-mod-styled:focus` style */
.jp-FormGroup-content fieldset input:focus,
.jp-FormGroup-content fieldset select:focus {
-moz-outline-radius: unset;
outline: var(--jp-border-width) solid var(--md-blue-500);
outline-offset: -1px;
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-FormGroup-content fieldset input:hover:not(:focus),
.jp-FormGroup-content fieldset select:hover:not(:focus) {
background-color: var(--jp-border-color2);
}
/* stylelint-enable selector-max-type */
.jp-FormGroup-content .checkbox .field-description {
/* Disable default description field for checkbox:
because other widgets do not have description fields,
we add descriptions to each widget on the field level.
*/
display: none;
}
.jp-FormGroup-content #root__description {
display: none;
}
.jp-FormGroup-content .jp-modifiedIndicator {
width: 5px;
background-color: var(--jp-brand-color2);
margin-top: 0;
margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
flex-shrink: 0;
}
.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
background-color: var(--jp-error-color0);
margin-right: 0.5em;
}
/* RJSF ARRAY style */
.jp-arrayFieldWrapper legend {
font-size: var(--jp-content-font-size2);
color: var(--jp-ui-font-color0);
flex-basis: 100%;
padding: 4px 0;
font-weight: var(--jp-content-heading-font-weight);
border-bottom: 1px solid var(--jp-border-color2);
}
.jp-arrayFieldWrapper .field-description {
padding: 4px 0;
white-space: pre-wrap;
}
.jp-arrayFieldWrapper .array-item {
width: 100%;
border: 1px solid var(--jp-border-color2);
border-radius: 4px;
margin: 4px;
}
.jp-ArrayOperations {
display: flex;
margin-left: 8px;
}
.jp-ArrayOperationsButton {
margin: 2px;
}
.jp-ArrayOperationsButton .jp-icon3[fill] {
fill: var(--jp-ui-font-color0);
}
button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
cursor: not-allowed;
opacity: 0.5;
}
/* RJSF form validation error */
.jp-FormGroup-content .validationErrors {
color: var(--jp-error-color0);
}
/* Hide panel level error as duplicated the field level error */
.jp-FormGroup-content .panel.errors {
display: none;
}
/* RJSF normal content (settings-editor) */
.jp-FormGroup-contentNormal {
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
margin-left: 7px;
color: var(--jp-ui-font-color0);
}
.jp-FormGroup-contentNormal .jp-FormGroup-description {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-default {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
font-size: var(--jp-content-font-size1);
font-weight: normal;
min-width: 120px;
}
.jp-FormGroup-contentNormal fieldset:not(:first-child) {
margin-left: 7px;
}
.jp-FormGroup-contentNormal .field-array-of-string .array-item {
/* Display `jp-ArrayOperations` buttons side-by-side with content except
for small screens where flex-wrap will place them one below the other.
*/
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
/* RJSF compact content (metadata-form) */
.jp-FormGroup-content.jp-FormGroup-contentCompact {
width: 100%;
}
.jp-FormGroup-contentCompact .form-group {
display: flex;
padding: 0.5em 0.2em 0.5em 0;
}
.jp-FormGroup-contentCompact
.jp-FormGroup-compactTitle
.jp-FormGroup-description {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color2);
}
.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
padding-bottom: 0.3em;
}
.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
width: 100%;
box-sizing: border-box;
}
.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
padding-bottom: 7px;
}
.jp-FormGroup-contentCompact
.jp-objectFieldWrapper
.jp-objectFieldWrapper
.form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
.jp-FormGroup-contentCompact ul.error-detail {
margin-block-start: 0.5em;
margin-block-end: 0.5em;
padding-inline-start: 1em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-SidePanel {
display: flex;
flex-direction: column;
min-width: var(--jp-sidebar-min-width);
overflow-y: auto;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
font-size: var(--jp-ui-font-size1);
}
.jp-SidePanel-header {
flex: 0 0 auto;
display: flex;
border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin: 0;
padding: 2px;
text-transform: uppercase;
}
.jp-SidePanel-toolbar {
flex: 0 0 auto;
}
.jp-SidePanel-content {
flex: 1 1 auto;
}
.jp-SidePanel-toolbar,
.jp-AccordionPanel-toolbar {
height: var(--jp-private-toolbar-height);
}
.jp-SidePanel-toolbar.jp-Toolbar-micro {
display: none;
}
.lm-AccordionPanel .jp-AccordionPanel-title {
box-sizing: border-box;
line-height: 25px;
margin: 0;
display: flex;
align-items: center;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
font-size: var(--jp-ui-font-size0);
}
.jp-AccordionPanel-title {
cursor: pointer;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
text-transform: uppercase;
}
.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
/* Title is rotated for horizontal accordion panel using CSS */
display: block;
transform-origin: top left;
transform: rotate(-90deg) translate(-100%);
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
user-select: none;
text-overflow: ellipsis;
white-space: nowrap;
overflow: hidden;
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
transform: rotate(-90deg);
margin: auto 0;
height: 16px;
}
.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
transform: rotate(0deg);
}
.lm-AccordionPanel .jp-AccordionPanel-toolbar {
background: none;
box-shadow: none;
border: none;
margin-left: auto;
}
.lm-AccordionPanel .lm-SplitPanel-handle:hover {
background: var(--jp-layout-color3);
}
.jp-text-truncated {
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Spinner {
position: absolute;
display: flex;
justify-content: center;
align-items: center;
z-index: 10;
left: 0;
top: 0;
width: 100%;
height: 100%;
background: var(--jp-layout-color0);
outline: none;
}
.jp-SpinnerContent {
font-size: 10px;
margin: 50px auto;
text-indent: -9999em;
width: 3em;
height: 3em;
border-radius: 50%;
background: var(--jp-brand-color3);
background: linear-gradient(
to right,
#f37626 10%,
rgba(255, 255, 255, 0) 42%
);
position: relative;
animation: load3 1s infinite linear, fadeIn 1s;
}
.jp-SpinnerContent::before {
width: 50%;
height: 50%;
background: #f37626;
border-radius: 100% 0 0;
position: absolute;
top: 0;
left: 0;
content: '';
}
.jp-SpinnerContent::after {
background: var(--jp-layout-color0);
width: 75%;
height: 75%;
border-radius: 50%;
content: '';
margin: auto;
position: absolute;
top: 0;
left: 0;
bottom: 0;
right: 0;
}
@keyframes fadeIn {
0% {
opacity: 0;
}
100% {
opacity: 1;
}
}
@keyframes load3 {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
button.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: none;
box-sizing: border-box;
text-align: center;
line-height: 32px;
height: 32px;
padding: 0 12px;
letter-spacing: 0.8px;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input.jp-mod-styled {
background: var(--jp-input-background);
height: 28px;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color1);
padding-left: 7px;
padding-right: 7px;
font-size: var(--jp-ui-font-size2);
color: var(--jp-ui-font-color0);
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input[type='checkbox'].jp-mod-styled {
appearance: checkbox;
-webkit-appearance: checkbox;
-moz-appearance: checkbox;
height: auto;
}
input.jp-mod-styled:focus {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-select-wrapper {
display: flex;
position: relative;
flex-direction: column;
padding: 1px;
background-color: var(--jp-layout-color1);
box-sizing: border-box;
margin-bottom: 12px;
}
.jp-select-wrapper:not(.multiple) {
height: 28px;
}
.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-input-active-background);
}
select.jp-mod-styled:hover {
cursor: pointer;
color: var(--jp-ui-font-color0);
background-color: var(--jp-input-hover-background);
box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
}
select.jp-mod-styled {
flex: 1 1 auto;
width: 100%;
font-size: var(--jp-ui-font-size2);
background: var(--jp-input-background);
color: var(--jp-ui-font-color0);
padding: 0 25px 0 8px;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
select.jp-mod-styled:not([multiple]) {
height: 32px;
}
select.jp-mod-styled[multiple] {
max-height: 200px;
overflow-y: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-switch {
display: flex;
align-items: center;
padding-left: 4px;
padding-right: 4px;
font-size: var(--jp-ui-font-size1);
background-color: transparent;
color: var(--jp-ui-font-color1);
border: none;
height: 20px;
}
.jp-switch:hover {
background-color: var(--jp-layout-color2);
}
.jp-switch-label {
margin-right: 5px;
font-family: var(--jp-ui-font-family);
}
.jp-switch-track {
cursor: pointer;
background-color: var(--jp-switch-color, var(--jp-border-color1));
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 34px;
height: 16px;
width: 35px;
position: relative;
}
.jp-switch-track::before {
content: '';
position: absolute;
height: 10px;
width: 10px;
margin: 3px;
left: 0;
background-color: var(--jp-ui-inverse-font-color1);
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 50%;
}
.jp-switch[aria-checked='true'] .jp-switch-track {
background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
}
.jp-switch[aria-checked='true'] .jp-switch-track::before {
/* track width (35) - margins (3 + 3) - thumb width (10) */
left: 19px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
:root {
--jp-private-toolbar-height: calc(
28px + var(--jp-border-width)
); /* leave 28px for content */
}
.jp-Toolbar {
color: var(--jp-ui-font-color1);
flex: 0 0 auto;
display: flex;
flex-direction: row;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: 2px;
z-index: 8;
overflow-x: hidden;
}
/* Toolbar items */
.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
flex-grow: 1;
flex-shrink: 1;
}
.jp-Toolbar-item.jp-Toolbar-kernelStatus {
display: inline-block;
width: 32px;
background-repeat: no-repeat;
background-position: center;
background-size: 16px;
}
.jp-Toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
display: flex;
padding-left: 1px;
padding-right: 1px;
font-size: var(--jp-ui-font-size1);
line-height: var(--jp-private-toolbar-height);
height: 100%;
}
/* Toolbar buttons */
/* This is the div we use to wrap the react component into a Widget */
div.jp-ToolbarButton {
color: transparent;
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0;
margin: 0;
}
button.jp-ToolbarButtonComponent {
background: var(--jp-layout-color1);
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0 6px;
margin: 0;
height: 24px;
border-radius: var(--jp-border-radius);
display: flex;
align-items: center;
text-align: center;
font-size: 14px;
min-width: unset;
min-height: unset;
}
button.jp-ToolbarButtonComponent:disabled {
opacity: 0.4;
}
button.jp-ToolbarButtonComponent > span {
padding: 0;
flex: 0 0 auto;
}
button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
font-size: var(--jp-ui-font-size1);
line-height: 100%;
padding-left: 2px;
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar.jp-Toolbar-micro {
padding: 0;
min-height: 0;
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar {
border: none;
box-shadow: none;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-WindowedPanel-outer {
position: relative;
overflow-y: auto;
}
.jp-WindowedPanel-inner {
position: relative;
}
.jp-WindowedPanel-window {
position: absolute;
left: 0;
right: 0;
overflow: visible;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/* Sibling imports */
body {
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
}
/* Disable native link decoration styles everywhere outside of dialog boxes */
a {
text-decoration: unset;
color: unset;
}
a:hover {
text-decoration: unset;
color: unset;
}
/* Accessibility for links inside dialog box text */
.jp-Dialog-content a {
text-decoration: revert;
color: var(--jp-content-link-color);
}
.jp-Dialog-content a:hover {
text-decoration: revert;
}
/* Styles for ui-components */
.jp-Button {
color: var(--jp-ui-font-color2);
border-radius: var(--jp-border-radius);
padding: 0 12px;
font-size: var(--jp-ui-font-size1);
/* Copy from blueprint 3 */
display: inline-flex;
flex-direction: row;
border: none;
cursor: pointer;
align-items: center;
justify-content: center;
text-align: left;
vertical-align: middle;
min-height: 30px;
min-width: 30px;
}
.jp-Button:disabled {
cursor: not-allowed;
}
.jp-Button:empty {
padding: 0 !important;
}
.jp-Button.jp-mod-small {
min-height: 24px;
min-width: 24px;
font-size: 12px;
padding: 0 7px;
}
/* Use our own theme for hover styles */
.jp-Button.jp-mod-minimal:hover {
background-color: var(--jp-layout-color2);
}
.jp-Button.jp-mod-minimal {
background: none;
}
.jp-InputGroup {
display: block;
position: relative;
}
.jp-InputGroup input {
box-sizing: border-box;
border: none;
border-radius: 0;
background-color: transparent;
color: var(--jp-ui-font-color0);
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
padding-bottom: 0;
padding-top: 0;
padding-left: 10px;
padding-right: 28px;
position: relative;
width: 100%;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
font-size: 14px;
font-weight: 400;
height: 30px;
line-height: 30px;
outline: none;
vertical-align: middle;
}
.jp-InputGroup input:focus {
box-shadow: inset 0 0 0 var(--jp-border-width)
var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-InputGroup input:disabled {
cursor: not-allowed;
resize: block;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input:disabled ~ span {
cursor: not-allowed;
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input::placeholder,
input::placeholder {
color: var(--jp-ui-font-color2);
}
.jp-InputGroupAction {
position: absolute;
bottom: 1px;
right: 0;
padding: 6px;
}
.jp-HTMLSelect.jp-DefaultStyle select {
background-color: initial;
border: none;
border-radius: 0;
box-shadow: none;
color: var(--jp-ui-font-color0);
display: block;
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
height: 24px;
line-height: 14px;
padding: 0 25px 0 10px;
text-align: left;
-moz-appearance: none;
-webkit-appearance: none;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
cursor: not-allowed;
resize: block;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
cursor: not-allowed;
}
/* Use our own theme for hover and option styles */
/* stylelint-disable-next-line selector-max-type */
.jp-HTMLSelect.jp-DefaultStyle select:hover,
.jp-HTMLSelect.jp-DefaultStyle select > option {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color0);
}
select {
box-sizing: border-box;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-StatusBar-Widget {
display: flex;
align-items: center;
background: var(--jp-layout-color2);
min-height: var(--jp-statusbar-height);
justify-content: space-between;
padding: 0 10px;
}
.jp-StatusBar-Left {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-StatusBar-Middle {
display: flex;
align-items: center;
}
.jp-StatusBar-Right {
display: flex;
align-items: center;
flex-direction: row-reverse;
}
.jp-StatusBar-Item {
max-height: var(--jp-statusbar-height);
margin: 0 2px;
height: var(--jp-statusbar-height);
white-space: nowrap;
text-overflow: ellipsis;
color: var(--jp-ui-font-color1);
padding: 0 6px;
}
.jp-mod-highlighted:hover {
background-color: var(--jp-layout-color3);
}
.jp-mod-clicked {
background-color: var(--jp-brand-color1);
}
.jp-mod-clicked:hover {
background-color: var(--jp-brand-color0);
}
.jp-mod-clicked .jp-StatusBar-TextItem {
color: var(--jp-ui-inverse-font-color1);
}
.jp-StatusBar-HoverItem {
box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
}
.jp-StatusBar-TextItem {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
line-height: 24px;
color: var(--jp-ui-font-color1);
}
.jp-StatusBar-GroupItem {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-Statusbar-ProgressCircle svg {
display: block;
margin: 0 auto;
width: 16px;
height: 24px;
align-self: normal;
}
.jp-Statusbar-ProgressCircle path {
fill: var(--jp-inverse-layout-color3);
}
.jp-Statusbar-ProgressBar-progress-bar {
height: 10px;
width: 100px;
border: solid 0.25px var(--jp-brand-color2);
border-radius: 3px;
overflow: hidden;
align-self: center;
}
.jp-Statusbar-ProgressBar-progress-bar > div {
background-color: var(--jp-brand-color2);
background-image: linear-gradient(
-45deg,
rgba(255, 255, 255, 0.2) 25%,
transparent 25%,
transparent 50%,
rgba(255, 255, 255, 0.2) 50%,
rgba(255, 255, 255, 0.2) 75%,
transparent 75%,
transparent
);
background-size: 40px 40px;
float: left;
width: 0%;
height: 100%;
font-size: 12px;
line-height: 14px;
color: #fff;
text-align: center;
animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
}
.jp-Statusbar-ProgressBar-progress-bar p {
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
font-size: var(--jp-ui-font-size1);
line-height: 10px;
width: 100px;
}
@keyframes jp-Statusbar-ExecutionTime-progress-bar {
0% {
background-position: 0 0;
}
100% {
background-position: 40px 40px;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-commandpalette-search-height: 28px;
}
/*-----------------------------------------------------------------------------
| Overall styles
|----------------------------------------------------------------------------*/
.lm-CommandPalette {
padding-bottom: 0;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Modal variant
|----------------------------------------------------------------------------*/
.jp-ModalCommandPalette {
position: absolute;
z-index: 10000;
top: 38px;
left: 30%;
margin: 0;
padding: 4px;
width: 40%;
box-shadow: var(--jp-elevation-z4);
border-radius: 4px;
background: var(--jp-layout-color0);
}
.jp-ModalCommandPalette .lm-CommandPalette {
max-height: 40vh;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
margin-left: 4px;
margin-right: 4px;
}
.jp-ModalCommandPalette
.lm-CommandPalette
.lm-CommandPalette-item.lm-mod-disabled {
display: none;
}
/*-----------------------------------------------------------------------------
| Search
|----------------------------------------------------------------------------*/
.lm-CommandPalette-search {
padding: 4px;
background-color: var(--jp-layout-color1);
z-index: 2;
}
.lm-CommandPalette-wrapper {
overflow: overlay;
padding: 0 9px;
background-color: var(--jp-input-active-background);
height: 30px;
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
}
.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-SearchIconGroup {
color: white;
background-color: var(--jp-brand-color1);
position: absolute;
top: 4px;
right: 4px;
padding: 5px 5px 1px;
}
.jp-SearchIconGroup svg {
height: 20px;
width: 20px;
}
.jp-SearchIconGroup .jp-icon3[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-input {
background: transparent;
width: calc(100% - 18px);
float: left;
border: none;
outline: none;
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
line-height: var(--jp-private-commandpalette-search-height);
}
.lm-CommandPalette-input::-webkit-input-placeholder,
.lm-CommandPalette-input::-moz-placeholder,
.lm-CommandPalette-input:-ms-input-placeholder {
color: var(--jp-ui-font-color2);
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Results
|----------------------------------------------------------------------------*/
.lm-CommandPalette-header:first-child {
margin-top: 0;
}
.lm-CommandPalette-header {
border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
color: var(--jp-ui-font-color1);
cursor: pointer;
display: flex;
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin-top: 8px;
padding: 8px 0 8px 12px;
text-transform: uppercase;
}
.lm-CommandPalette-header.lm-mod-active {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-header > mark {
background-color: transparent;
font-weight: bold;
color: var(--jp-ui-font-color1);
}
.lm-CommandPalette-item {
padding: 4px 12px 4px 4px;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
font-weight: 400;
display: flex;
}
.lm-CommandPalette-item.lm-mod-disabled {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item.lm-mod-active {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-inverse-font-color0);
}
.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-itemContent {
overflow: hidden;
}
.lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.lm-CommandPalette-item.lm-mod-disabled mark {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
margin: 0 4px 0 0;
position: relative;
width: 16px;
top: 2px;
flex: 0 0 auto;
}
.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
opacity: 0.6;
}
.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
flex: 0 0 auto;
}
.lm-CommandPalette-itemCaption {
display: none;
}
.lm-CommandPalette-content {
background-color: var(--jp-layout-color1);
}
.lm-CommandPalette-content:empty::after {
content: 'No results';
margin: auto;
margin-top: 20px;
width: 100px;
display: block;
font-size: var(--jp-ui-font-size2);
font-family: var(--jp-ui-font-family);
font-weight: lighter;
}
.lm-CommandPalette-emptyMessage {
text-align: center;
margin-top: 24px;
line-height: 1.32;
padding: 0 8px;
color: var(--jp-content-font-color3);
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Dialog {
position: absolute;
z-index: 10000;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
top: 0;
left: 0;
margin: 0;
padding: 0;
width: 100%;
height: 100%;
background: var(--jp-dialog-background);
}
.jp-Dialog-content {
display: flex;
flex-direction: column;
margin-left: auto;
margin-right: auto;
background: var(--jp-layout-color1);
padding: 24px 24px 12px;
min-width: 300px;
min-height: 150px;
max-width: 1000px;
max-height: 500px;
box-sizing: border-box;
box-shadow: var(--jp-elevation-z20);
word-wrap: break-word;
border-radius: var(--jp-border-radius);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color1);
resize: both;
}
.jp-Dialog-content.jp-Dialog-content-small {
max-width: 500px;
}
.jp-Dialog-button {
overflow: visible;
}
button.jp-Dialog-button:focus {
outline: 1px solid var(--jp-brand-color1);
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button:focus::-moz-focus-inner {
border: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
}
button.jp-Dialog-close-button {
padding: 0;
height: 100%;
min-width: unset;
min-height: unset;
}
.jp-Dialog-header {
display: flex;
justify-content: space-between;
flex: 0 0 auto;
padding-bottom: 12px;
font-size: var(--jp-ui-font-size3);
font-weight: 400;
color: var(--jp-ui-font-color1);
}
.jp-Dialog-body {
display: flex;
flex-direction: column;
flex: 1 1 auto;
font-size: var(--jp-ui-font-size1);
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
overflow: auto;
}
.jp-Dialog-footer {
display: flex;
flex-direction: row;
justify-content: flex-end;
align-items: center;
flex: 0 0 auto;
margin-left: -12px;
margin-right: -12px;
padding: 12px;
}
.jp-Dialog-checkbox {
padding-right: 5px;
}
.jp-Dialog-checkbox > input:focus-visible {
outline: 1px solid var(--jp-input-active-border-color);
outline-offset: 1px;
}
.jp-Dialog-spacer {
flex: 1 1 auto;
}
.jp-Dialog-title {
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
}
.jp-Dialog-body > .jp-select-wrapper {
width: 100%;
}
.jp-Dialog-body > button {
padding: 0 16px;
}
.jp-Dialog-body > label {
line-height: 1.4;
color: var(--jp-ui-font-color0);
}
.jp-Dialog-button.jp-mod-styled:not(:last-child) {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Input-Boolean-Dialog {
flex-direction: row-reverse;
align-items: end;
width: 100%;
}
.jp-Input-Boolean-Dialog > label {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MainAreaWidget > :focus {
outline: none;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error {
padding: 6px;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
width: auto;
padding: 10px;
background: var(--jp-error-color3);
border: var(--jp-border-width) solid var(--jp-error-color1);
border-radius: var(--jp-border-radius);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
white-space: pre-wrap;
word-wrap: break-word;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/**
* google-material-color v1.2.6
* https://github.com/danlevan/google-material-color
*/
:root {
--md-red-50: #ffebee;
--md-red-100: #ffcdd2;
--md-red-200: #ef9a9a;
--md-red-300: #e57373;
--md-red-400: #ef5350;
--md-red-500: #f44336;
--md-red-600: #e53935;
--md-red-700: #d32f2f;
--md-red-800: #c62828;
--md-red-900: #b71c1c;
--md-red-A100: #ff8a80;
--md-red-A200: #ff5252;
--md-red-A400: #ff1744;
--md-red-A700: #d50000;
--md-pink-50: #fce4ec;
--md-pink-100: #f8bbd0;
--md-pink-200: #f48fb1;
--md-pink-300: #f06292;
--md-pink-400: #ec407a;
--md-pink-500: #e91e63;
--md-pink-600: #d81b60;
--md-pink-700: #c2185b;
--md-pink-800: #ad1457;
--md-pink-900: #880e4f;
--md-pink-A100: #ff80ab;
--md-pink-A200: #ff4081;
--md-pink-A400: #f50057;
--md-pink-A700: #c51162;
--md-purple-50: #f3e5f5;
--md-purple-100: #e1bee7;
--md-purple-200: #ce93d8;
--md-purple-300: #ba68c8;
--md-purple-400: #ab47bc;
--md-purple-500: #9c27b0;
--md-purple-600: #8e24aa;
--md-purple-700: #7b1fa2;
--md-purple-800: #6a1b9a;
--md-purple-900: #4a148c;
--md-purple-A100: #ea80fc;
--md-purple-A200: #e040fb;
--md-purple-A400: #d500f9;
--md-purple-A700: #a0f;
--md-deep-purple-50: #ede7f6;
--md-deep-purple-100: #d1c4e9;
--md-deep-purple-200: #b39ddb;
--md-deep-purple-300: #9575cd;
--md-deep-purple-400: #7e57c2;
--md-deep-purple-500: #673ab7;
--md-deep-purple-600: #5e35b1;
--md-deep-purple-700: #512da8;
--md-deep-purple-800: #4527a0;
--md-deep-purple-900: #311b92;
--md-deep-purple-A100: #b388ff;
--md-deep-purple-A200: #7c4dff;
--md-deep-purple-A400: #651fff;
--md-deep-purple-A700: #6200ea;
--md-indigo-50: #e8eaf6;
--md-indigo-100: #c5cae9;
--md-indigo-200: #9fa8da;
--md-indigo-300: #7986cb;
--md-indigo-400: #5c6bc0;
--md-indigo-500: #3f51b5;
--md-indigo-600: #3949ab;
--md-indigo-700: #303f9f;
--md-indigo-800: #283593;
--md-indigo-900: #1a237e;
--md-indigo-A100: #8c9eff;
--md-indigo-A200: #536dfe;
--md-indigo-A400: #3d5afe;
--md-indigo-A700: #304ffe;
--md-blue-50: #e3f2fd;
--md-blue-100: #bbdefb;
--md-blue-200: #90caf9;
--md-blue-300: #64b5f6;
--md-blue-400: #42a5f5;
--md-blue-500: #2196f3;
--md-blue-600: #1e88e5;
--md-blue-700: #1976d2;
--md-blue-800: #1565c0;
--md-blue-900: #0d47a1;
--md-blue-A100: #82b1ff;
--md-blue-A200: #448aff;
--md-blue-A400: #2979ff;
--md-blue-A700: #2962ff;
--md-light-blue-50: #e1f5fe;
--md-light-blue-100: #b3e5fc;
--md-light-blue-200: #81d4fa;
--md-light-blue-300: #4fc3f7;
--md-light-blue-400: #29b6f6;
--md-light-blue-500: #03a9f4;
--md-light-blue-600: #039be5;
--md-light-blue-700: #0288d1;
--md-light-blue-800: #0277bd;
--md-light-blue-900: #01579b;
--md-light-blue-A100: #80d8ff;
--md-light-blue-A200: #40c4ff;
--md-light-blue-A400: #00b0ff;
--md-light-blue-A700: #0091ea;
--md-cyan-50: #e0f7fa;
--md-cyan-100: #b2ebf2;
--md-cyan-200: #80deea;
--md-cyan-300: #4dd0e1;
--md-cyan-400: #26c6da;
--md-cyan-500: #00bcd4;
--md-cyan-600: #00acc1;
--md-cyan-700: #0097a7;
--md-cyan-800: #00838f;
--md-cyan-900: #006064;
--md-cyan-A100: #84ffff;
--md-cyan-A200: #18ffff;
--md-cyan-A400: #00e5ff;
--md-cyan-A700: #00b8d4;
--md-teal-50: #e0f2f1;
--md-teal-100: #b2dfdb;
--md-teal-200: #80cbc4;
--md-teal-300: #4db6ac;
--md-teal-400: #26a69a;
--md-teal-500: #009688;
--md-teal-600: #00897b;
--md-teal-700: #00796b;
--md-teal-800: #00695c;
--md-teal-900: #004d40;
--md-teal-A100: #a7ffeb;
--md-teal-A200: #64ffda;
--md-teal-A400: #1de9b6;
--md-teal-A700: #00bfa5;
--md-green-50: #e8f5e9;
--md-green-100: #c8e6c9;
--md-green-200: #a5d6a7;
--md-green-300: #81c784;
--md-green-400: #66bb6a;
--md-green-500: #4caf50;
--md-green-600: #43a047;
--md-green-700: #388e3c;
--md-green-800: #2e7d32;
--md-green-900: #1b5e20;
--md-green-A100: #b9f6ca;
--md-green-A200: #69f0ae;
--md-green-A400: #00e676;
--md-green-A700: #00c853;
--md-light-green-50: #f1f8e9;
--md-light-green-100: #dcedc8;
--md-light-green-200: #c5e1a5;
--md-light-green-300: #aed581;
--md-light-green-400: #9ccc65;
--md-light-green-500: #8bc34a;
--md-light-green-600: #7cb342;
--md-light-green-700: #689f38;
--md-light-green-800: #558b2f;
--md-light-green-900: #33691e;
--md-light-green-A100: #ccff90;
--md-light-green-A200: #b2ff59;
--md-light-green-A400: #76ff03;
--md-light-green-A700: #64dd17;
--md-lime-50: #f9fbe7;
--md-lime-100: #f0f4c3;
--md-lime-200: #e6ee9c;
--md-lime-300: #dce775;
--md-lime-400: #d4e157;
--md-lime-500: #cddc39;
--md-lime-600: #c0ca33;
--md-lime-700: #afb42b;
--md-lime-800: #9e9d24;
--md-lime-900: #827717;
--md-lime-A100: #f4ff81;
--md-lime-A200: #eeff41;
--md-lime-A400: #c6ff00;
--md-lime-A700: #aeea00;
--md-yellow-50: #fffde7;
--md-yellow-100: #fff9c4;
--md-yellow-200: #fff59d;
--md-yellow-300: #fff176;
--md-yellow-400: #ffee58;
--md-yellow-500: #ffeb3b;
--md-yellow-600: #fdd835;
--md-yellow-700: #fbc02d;
--md-yellow-800: #f9a825;
--md-yellow-900: #f57f17;
--md-yellow-A100: #ffff8d;
--md-yellow-A200: #ff0;
--md-yellow-A400: #ffea00;
--md-yellow-A700: #ffd600;
--md-amber-50: #fff8e1;
--md-amber-100: #ffecb3;
--md-amber-200: #ffe082;
--md-amber-300: #ffd54f;
--md-amber-400: #ffca28;
--md-amber-500: #ffc107;
--md-amber-600: #ffb300;
--md-amber-700: #ffa000;
--md-amber-800: #ff8f00;
--md-amber-900: #ff6f00;
--md-amber-A100: #ffe57f;
--md-amber-A200: #ffd740;
--md-amber-A400: #ffc400;
--md-amber-A700: #ffab00;
--md-orange-50: #fff3e0;
--md-orange-100: #ffe0b2;
--md-orange-200: #ffcc80;
--md-orange-300: #ffb74d;
--md-orange-400: #ffa726;
--md-orange-500: #ff9800;
--md-orange-600: #fb8c00;
--md-orange-700: #f57c00;
--md-orange-800: #ef6c00;
--md-orange-900: #e65100;
--md-orange-A100: #ffd180;
--md-orange-A200: #ffab40;
--md-orange-A400: #ff9100;
--md-orange-A700: #ff6d00;
--md-deep-orange-50: #fbe9e7;
--md-deep-orange-100: #ffccbc;
--md-deep-orange-200: #ffab91;
--md-deep-orange-300: #ff8a65;
--md-deep-orange-400: #ff7043;
--md-deep-orange-500: #ff5722;
--md-deep-orange-600: #f4511e;
--md-deep-orange-700: #e64a19;
--md-deep-orange-800: #d84315;
--md-deep-orange-900: #bf360c;
--md-deep-orange-A100: #ff9e80;
--md-deep-orange-A200: #ff6e40;
--md-deep-orange-A400: #ff3d00;
--md-deep-orange-A700: #dd2c00;
--md-brown-50: #efebe9;
--md-brown-100: #d7ccc8;
--md-brown-200: #bcaaa4;
--md-brown-300: #a1887f;
--md-brown-400: #8d6e63;
--md-brown-500: #795548;
--md-brown-600: #6d4c41;
--md-brown-700: #5d4037;
--md-brown-800: #4e342e;
--md-brown-900: #3e2723;
--md-grey-50: #fafafa;
--md-grey-100: #f5f5f5;
--md-grey-200: #eee;
--md-grey-300: #e0e0e0;
--md-grey-400: #bdbdbd;
--md-grey-500: #9e9e9e;
--md-grey-600: #757575;
--md-grey-700: #616161;
--md-grey-800: #424242;
--md-grey-900: #212121;
--md-blue-grey-50: #eceff1;
--md-blue-grey-100: #cfd8dc;
--md-blue-grey-200: #b0bec5;
--md-blue-grey-300: #90a4ae;
--md-blue-grey-400: #78909c;
--md-blue-grey-500: #607d8b;
--md-blue-grey-600: #546e7a;
--md-blue-grey-700: #455a64;
--md-blue-grey-800: #37474f;
--md-blue-grey-900: #263238;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| RenderedText
|----------------------------------------------------------------------------*/
:root {
/* This is the padding value to fill the gaps between lines containing spans with background color. */
--jp-private-code-span-padding: calc(
(var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
);
}
.jp-RenderedText {
text-align: left;
padding-left: var(--jp-code-padding);
line-height: var(--jp-code-line-height);
font-family: var(--jp-code-font-family);
}
.jp-RenderedText pre,
.jp-RenderedJavaScript pre,
.jp-RenderedHTMLCommon pre {
color: var(--jp-content-font-color1);
font-size: var(--jp-code-font-size);
border: none;
margin: 0;
padding: 0;
}
.jp-RenderedText pre a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* console foregrounds and backgrounds */
.jp-RenderedText pre .ansi-black-fg {
color: #3e424d;
}
.jp-RenderedText pre .ansi-red-fg {
color: #e75c58;
}
.jp-RenderedText pre .ansi-green-fg {
color: #00a250;
}
.jp-RenderedText pre .ansi-yellow-fg {
color: #ddb62b;
}
.jp-RenderedText pre .ansi-blue-fg {
color: #208ffb;
}
.jp-RenderedText pre .ansi-magenta-fg {
color: #d160c4;
}
.jp-RenderedText pre .ansi-cyan-fg {
color: #60c6c8;
}
.jp-RenderedText pre .ansi-white-fg {
color: #c5c1b4;
}
.jp-RenderedText pre .ansi-black-bg {
background-color: #3e424d;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-bg {
background-color: #e75c58;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-bg {
background-color: #00a250;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-bg {
background-color: #ddb62b;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-bg {
background-color: #208ffb;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-bg {
background-color: #d160c4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-bg {
background-color: #60c6c8;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-bg {
background-color: #c5c1b4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-black-intense-fg {
color: #282c36;
}
.jp-RenderedText pre .ansi-red-intense-fg {
color: #b22b31;
}
.jp-RenderedText pre .ansi-green-intense-fg {
color: #007427;
}
.jp-RenderedText pre .ansi-yellow-intense-fg {
color: #b27d12;
}
.jp-RenderedText pre .ansi-blue-intense-fg {
color: #0065ca;
}
.jp-RenderedText pre .ansi-magenta-intense-fg {
color: #a03196;
}
.jp-RenderedText pre .ansi-cyan-intense-fg {
color: #258f8f;
}
.jp-RenderedText pre .ansi-white-intense-fg {
color: #a1a6b2;
}
.jp-RenderedText pre .ansi-black-intense-bg {
background-color: #282c36;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-intense-bg {
background-color: #b22b31;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-intense-bg {
background-color: #007427;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-intense-bg {
background-color: #b27d12;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-intense-bg {
background-color: #0065ca;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-intense-bg {
background-color: #a03196;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-intense-bg {
background-color: #258f8f;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-intense-bg {
background-color: #a1a6b2;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-default-inverse-fg {
color: var(--jp-ui-inverse-font-color0);
}
.jp-RenderedText pre .ansi-default-inverse-bg {
background-color: var(--jp-inverse-layout-color0);
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-bold {
font-weight: bold;
}
.jp-RenderedText pre .ansi-underline {
text-decoration: underline;
}
.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
background: var(--jp-rendermime-error-background);
padding-top: var(--jp-code-padding);
}
/*-----------------------------------------------------------------------------
| RenderedLatex
|----------------------------------------------------------------------------*/
.jp-RenderedLatex {
color: var(--jp-content-font-color1);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
}
/* Left-justify outputs.*/
.jp-OutputArea-output.jp-RenderedLatex {
padding: var(--jp-code-padding);
text-align: left;
}
/*-----------------------------------------------------------------------------
| RenderedHTML
|----------------------------------------------------------------------------*/
.jp-RenderedHTMLCommon {
color: var(--jp-content-font-color1);
font-family: var(--jp-content-font-family);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
/* Give a bit more R padding on Markdown text to keep line lengths reasonable */
padding-right: 20px;
}
.jp-RenderedHTMLCommon em {
font-style: italic;
}
.jp-RenderedHTMLCommon strong {
font-weight: bold;
}
.jp-RenderedHTMLCommon u {
text-decoration: underline;
}
.jp-RenderedHTMLCommon a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* Headings */
.jp-RenderedHTMLCommon h1,
.jp-RenderedHTMLCommon h2,
.jp-RenderedHTMLCommon h3,
.jp-RenderedHTMLCommon h4,
.jp-RenderedHTMLCommon h5,
.jp-RenderedHTMLCommon h6 {
line-height: var(--jp-content-heading-line-height);
font-weight: var(--jp-content-heading-font-weight);
font-style: normal;
margin: var(--jp-content-heading-margin-top) 0
var(--jp-content-heading-margin-bottom) 0;
}
.jp-RenderedHTMLCommon h1:first-child,
.jp-RenderedHTMLCommon h2:first-child,
.jp-RenderedHTMLCommon h3:first-child,
.jp-RenderedHTMLCommon h4:first-child,
.jp-RenderedHTMLCommon h5:first-child,
.jp-RenderedHTMLCommon h6:first-child {
margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
}
.jp-RenderedHTMLCommon h1:last-child,
.jp-RenderedHTMLCommon h2:last-child,
.jp-RenderedHTMLCommon h3:last-child,
.jp-RenderedHTMLCommon h4:last-child,
.jp-RenderedHTMLCommon h5:last-child,
.jp-RenderedHTMLCommon h6:last-child {
margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
}
.jp-RenderedHTMLCommon h1 {
font-size: var(--jp-content-font-size5);
}
.jp-RenderedHTMLCommon h2 {
font-size: var(--jp-content-font-size4);
}
.jp-RenderedHTMLCommon h3 {
font-size: var(--jp-content-font-size3);
}
.jp-RenderedHTMLCommon h4 {
font-size: var(--jp-content-font-size2);
}
.jp-RenderedHTMLCommon h5 {
font-size: var(--jp-content-font-size1);
}
.jp-RenderedHTMLCommon h6 {
font-size: var(--jp-content-font-size0);
}
/* Lists */
/* stylelint-disable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon ul:not(.list-inline),
.jp-RenderedHTMLCommon ol:not(.list-inline) {
padding-left: 2em;
}
.jp-RenderedHTMLCommon ul {
list-style: disc;
}
.jp-RenderedHTMLCommon ul ul {
list-style: square;
}
.jp-RenderedHTMLCommon ul ul ul {
list-style: circle;
}
.jp-RenderedHTMLCommon ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol ol {
list-style: upper-alpha;
}
.jp-RenderedHTMLCommon ol ol ol {
list-style: lower-alpha;
}
.jp-RenderedHTMLCommon ol ol ol ol {
list-style: lower-roman;
}
.jp-RenderedHTMLCommon ol ol ol ol ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol,
.jp-RenderedHTMLCommon ul {
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon ul ul,
.jp-RenderedHTMLCommon ul ol,
.jp-RenderedHTMLCommon ol ul,
.jp-RenderedHTMLCommon ol ol {
margin-bottom: 0;
}
/* stylelint-enable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon hr {
color: var(--jp-border-color2);
background-color: var(--jp-border-color1);
margin-top: 1em;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon > pre {
margin: 1.5em 2em;
}
.jp-RenderedHTMLCommon pre,
.jp-RenderedHTMLCommon code {
border: 0;
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
font-family: var(--jp-code-font-family);
font-size: inherit;
line-height: var(--jp-code-line-height);
padding: 0;
white-space: pre-wrap;
}
.jp-RenderedHTMLCommon :not(pre) > code {
background-color: var(--jp-layout-color2);
padding: 1px 5px;
}
/* Tables */
.jp-RenderedHTMLCommon table {
border-collapse: collapse;
border-spacing: 0;
border: none;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
table-layout: fixed;
margin-left: auto;
margin-bottom: 1em;
margin-right: auto;
}
.jp-RenderedHTMLCommon thead {
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
vertical-align: bottom;
}
.jp-RenderedHTMLCommon td,
.jp-RenderedHTMLCommon th,
.jp-RenderedHTMLCommon tr {
vertical-align: middle;
padding: 0.5em;
line-height: normal;
white-space: normal;
max-width: none;
border: none;
}
.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
max-width: none;
}
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
text-align: right;
}
.jp-RenderedHTMLCommon th {
font-weight: bold;
}
.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
background: var(--jp-layout-color0);
}
.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
background: var(--jp-rendermime-table-row-background);
}
.jp-RenderedHTMLCommon tbody tr:hover {
background: var(--jp-rendermime-table-row-hover-background);
}
.jp-RenderedHTMLCommon p {
text-align: left;
margin: 0;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon img {
-moz-force-broken-image-icon: 1;
}
/* Restrict to direct children as other images could be nested in other content. */
.jp-RenderedHTMLCommon > img {
display: block;
margin-left: 0;
margin-right: 0;
margin-bottom: 1em;
}
/* Change color behind transparent images if they need it... */
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
background-color: var(--jp-inverse-layout-color1);
}
[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
background-color: var(--jp-inverse-layout-color1);
}
.jp-RenderedHTMLCommon img,
.jp-RenderedImage img,
.jp-RenderedHTMLCommon svg,
.jp-RenderedSVG svg {
max-width: 100%;
height: auto;
}
.jp-RenderedHTMLCommon img.jp-mod-unconfined,
.jp-RenderedImage img.jp-mod-unconfined,
.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
.jp-RenderedSVG svg.jp-mod-unconfined {
max-width: none;
}
.jp-RenderedHTMLCommon .alert {
padding: var(--jp-notebook-padding);
border: var(--jp-border-width) solid transparent;
border-radius: var(--jp-border-radius);
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon .alert-info {
color: var(--jp-info-color0);
background-color: var(--jp-info-color3);
border-color: var(--jp-info-color2);
}
.jp-RenderedHTMLCommon .alert-info hr {
border-color: var(--jp-info-color3);
}
.jp-RenderedHTMLCommon .alert-info > p:last-child,
.jp-RenderedHTMLCommon .alert-info > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-warning {
color: var(--jp-warn-color0);
background-color: var(--jp-warn-color3);
border-color: var(--jp-warn-color2);
}
.jp-RenderedHTMLCommon .alert-warning hr {
border-color: var(--jp-warn-color3);
}
.jp-RenderedHTMLCommon .alert-warning > p:last-child,
.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-success {
color: var(--jp-success-color0);
background-color: var(--jp-success-color3);
border-color: var(--jp-success-color2);
}
.jp-RenderedHTMLCommon .alert-success hr {
border-color: var(--jp-success-color3);
}
.jp-RenderedHTMLCommon .alert-success > p:last-child,
.jp-RenderedHTMLCommon .alert-success > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-danger {
color: var(--jp-error-color0);
background-color: var(--jp-error-color3);
border-color: var(--jp-error-color2);
}
.jp-RenderedHTMLCommon .alert-danger hr {
border-color: var(--jp-error-color3);
}
.jp-RenderedHTMLCommon .alert-danger > p:last-child,
.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon blockquote {
margin: 1em 2em;
padding: 0 1em;
border-left: 5px solid var(--jp-border-color2);
}
a.jp-InternalAnchorLink {
visibility: hidden;
margin-left: 8px;
color: var(--md-blue-800);
}
h1:hover .jp-InternalAnchorLink,
h2:hover .jp-InternalAnchorLink,
h3:hover .jp-InternalAnchorLink,
h4:hover .jp-InternalAnchorLink,
h5:hover .jp-InternalAnchorLink,
h6:hover .jp-InternalAnchorLink {
visibility: visible;
}
.jp-RenderedHTMLCommon kbd {
background-color: var(--jp-rendermime-table-row-background);
border: 1px solid var(--jp-border-color0);
border-bottom-color: var(--jp-border-color2);
border-radius: 3px;
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
display: inline-block;
font-size: var(--jp-ui-font-size0);
line-height: 1em;
padding: 0.2em 0.5em;
}
/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
* At the bottom of cells this is a bit too much as there is also spacing
* between cells. Going all the way to 0 gets too tight between markdown and
* code cells.
*/
.jp-RenderedHTMLCommon > *:last-child {
margin-bottom: 0.5em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
.lm-cursor-backdrop {
position: fixed;
width: 200px;
height: 200px;
margin-top: -100px;
margin-left: -100px;
will-change: transform;
z-index: 100;
}
.lm-mod-drag-image {
will-change: transform;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-lineFormSearch {
padding: 4px 12px;
background-color: var(--jp-layout-color2);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
font-size: var(--jp-ui-font-size1);
}
.jp-lineFormCaption {
font-size: var(--jp-ui-font-size0);
line-height: var(--jp-ui-font-size1);
margin-top: 4px;
color: var(--jp-ui-font-color0);
}
.jp-baseLineForm {
border: none;
border-radius: 0;
position: absolute;
background-size: 16px;
background-repeat: no-repeat;
background-position: center;
outline: none;
}
.jp-lineFormButtonContainer {
top: 4px;
right: 8px;
height: 24px;
padding: 0 12px;
width: 12px;
}
.jp-lineFormButtonIcon {
top: 0;
right: 0;
background-color: var(--jp-brand-color1);
height: 100%;
width: 100%;
box-sizing: border-box;
padding: 4px 6px;
}
.jp-lineFormButton {
top: 0;
right: 0;
background-color: transparent;
height: 100%;
width: 100%;
box-sizing: border-box;
}
.jp-lineFormWrapper {
overflow: hidden;
padding: 0 8px;
border: 1px solid var(--jp-border-color0);
background-color: var(--jp-input-active-background);
height: 22px;
}
.jp-lineFormWrapperFocusWithin {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-lineFormInput {
background: transparent;
width: 200px;
height: 100%;
border: none;
outline: none;
color: var(--jp-ui-font-color0);
line-height: 28px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-JSONEditor {
display: flex;
flex-direction: column;
width: 100%;
}
.jp-JSONEditor-host {
flex: 1 1 auto;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
background: var(--jp-layout-color0);
min-height: 50px;
padding: 1px;
}
.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
border-color: red;
outline-color: red;
}
.jp-JSONEditor-header {
display: flex;
flex: 1 0 auto;
padding: 0 0 0 12px;
}
.jp-JSONEditor-header label {
flex: 0 0 auto;
}
.jp-JSONEditor-commitButton {
height: 16px;
width: 16px;
background-size: 18px;
background-repeat: no-repeat;
background-position: center;
}
.jp-JSONEditor-host.jp-mod-focused {
background-color: var(--jp-input-active-background);
border: 1px solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
.jp-Editor.jp-mod-dropTarget {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-DocumentSearch-input {
border: none;
outline: none;
color: var(--jp-ui-font-color0);
font-size: var(--jp-ui-font-size1);
background-color: var(--jp-layout-color0);
font-family: var(--jp-ui-font-family);
padding: 2px 1px;
resize: none;
}
.jp-DocumentSearch-overlay {
position: absolute;
background-color: var(--jp-toolbar-background);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color);
top: 0;
right: 0;
z-index: 7;
min-width: 405px;
padding: 2px;
font-size: var(--jp-ui-font-size1);
--jp-private-document-search-button-height: 20px;
}
.jp-DocumentSearch-overlay button {
background-color: var(--jp-toolbar-background);
outline: 0;
}
.jp-DocumentSearch-overlay button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-overlay button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-overlay-row {
display: flex;
align-items: center;
margin-bottom: 2px;
}
.jp-DocumentSearch-button-content {
display: inline-block;
cursor: pointer;
box-sizing: border-box;
width: 100%;
height: 100%;
}
.jp-DocumentSearch-button-content svg {
width: 100%;
height: 100%;
}
.jp-DocumentSearch-input-wrapper {
border: var(--jp-border-width) solid var(--jp-border-color0);
display: flex;
background-color: var(--jp-layout-color0);
margin: 2px;
}
.jp-DocumentSearch-input-wrapper:focus-within {
border-color: var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper {
all: initial;
overflow: hidden;
display: inline-block;
border: none;
box-sizing: border-box;
}
.jp-DocumentSearch-toggle-wrapper {
width: 14px;
height: 14px;
}
.jp-DocumentSearch-button-wrapper {
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
}
.jp-DocumentSearch-toggle-wrapper:focus,
.jp-DocumentSearch-button-wrapper:focus {
outline: var(--jp-border-width) solid
var(--jp-cell-editor-active-border-color);
outline-offset: -1px;
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper,
.jp-DocumentSearch-button-content:focus {
outline: none;
}
.jp-DocumentSearch-toggle-placeholder {
width: 5px;
}
.jp-DocumentSearch-input-button::before {
display: block;
padding-top: 100%;
}
.jp-DocumentSearch-input-button-off {
opacity: var(--jp-search-toggle-off-opacity);
}
.jp-DocumentSearch-input-button-off:hover {
opacity: var(--jp-search-toggle-hover-opacity);
}
.jp-DocumentSearch-input-button-on {
opacity: var(--jp-search-toggle-on-opacity);
}
.jp-DocumentSearch-index-counter {
padding-left: 10px;
padding-right: 10px;
user-select: none;
min-width: 35px;
display: inline-block;
}
.jp-DocumentSearch-up-down-wrapper {
display: inline-block;
padding-right: 2px;
margin-left: auto;
white-space: nowrap;
}
.jp-DocumentSearch-spacer {
margin-left: auto;
}
.jp-DocumentSearch-up-down-wrapper button {
outline: 0;
border: none;
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
vertical-align: middle;
margin: 1px 5px 2px;
}
.jp-DocumentSearch-up-down-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-up-down-button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-filter-button {
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-filter-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled:hover {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-search-options {
padding: 0 8px;
margin-left: 3px;
width: 100%;
display: grid;
justify-content: start;
grid-template-columns: 1fr 1fr;
align-items: center;
justify-items: stretch;
}
.jp-DocumentSearch-search-filter-disabled {
color: var(--jp-ui-font-color2);
}
.jp-DocumentSearch-search-filter {
display: flex;
align-items: center;
user-select: none;
}
.jp-DocumentSearch-regex-error {
color: var(--jp-error-color0);
}
.jp-DocumentSearch-replace-button-wrapper {
overflow: hidden;
display: inline-block;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color0);
margin: auto 2px;
padding: 1px 4px;
height: calc(var(--jp-private-document-search-button-height) + 2px);
}
.jp-DocumentSearch-replace-button-wrapper:focus {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-replace-button {
display: inline-block;
text-align: center;
cursor: pointer;
box-sizing: border-box;
color: var(--jp-ui-font-color1);
/* height - 2 * (padding of wrapper) */
line-height: calc(var(--jp-private-document-search-button-height) - 2px);
width: 100%;
height: 100%;
}
.jp-DocumentSearch-replace-button:focus {
outline: none;
}
.jp-DocumentSearch-replace-wrapper-class {
margin-left: 14px;
display: flex;
}
.jp-DocumentSearch-replace-toggle {
border: none;
background-color: var(--jp-toolbar-background);
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-replace-toggle:hover {
background-color: var(--jp-layout-color2);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.cm-editor {
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
border: 0;
border-radius: 0;
height: auto;
/* Changed to auto to autogrow */
}
.cm-editor pre {
padding: 0 var(--jp-code-padding);
}
.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog {
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
}
.jp-CodeMirrorEditor {
cursor: text;
}
/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
@media screen and (min-width: 2138px) and (max-width: 4319px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width1) solid
var(--jp-editor-cursor-color);
}
}
/* When zoomed out less than 33% */
@media screen and (min-width: 4320px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width2) solid
var(--jp-editor-cursor-color);
}
}
.cm-editor.jp-mod-readOnly .cm-cursor {
display: none;
}
.jp-CollaboratorCursor {
border-left: 5px solid transparent;
border-right: 5px solid transparent;
border-top: none;
border-bottom: 3px solid;
background-clip: content-box;
margin-left: -5px;
margin-right: -5px;
}
.cm-searching,
.cm-searching span {
/* `.cm-searching span`: we need to override syntax highlighting */
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.cm-searching::selection,
.cm-searching span::selection {
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.jp-current-match > .cm-searching,
.jp-current-match > .cm-searching span,
.cm-searching > .jp-current-match,
.cm-searching > .jp-current-match span {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.jp-current-match > .cm-searching::selection,
.cm-searching > .jp-current-match::selection,
.jp-current-match > .cm-searching span::selection {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.cm-trailingspace {
background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
background-position: center left;
background-repeat: repeat-x;
}
.jp-CollaboratorCursor-hover {
position: absolute;
z-index: 1;
transform: translateX(-50%);
color: white;
border-radius: 3px;
padding-left: 4px;
padding-right: 4px;
padding-top: 1px;
padding-bottom: 1px;
text-align: center;
font-size: var(--jp-ui-font-size1);
white-space: nowrap;
}
.jp-CodeMirror-ruler {
border-left: 1px dashed var(--jp-border-color2);
}
/* Styles for shared cursors (remote cursor locations and selected ranges) */
.jp-CodeMirrorEditor .cm-ySelectionCaret {
position: relative;
border-left: 1px solid black;
margin-left: -1px;
margin-right: -1px;
box-sizing: border-box;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
white-space: nowrap;
position: absolute;
top: -1.15em;
padding-bottom: 0.05em;
left: -1px;
font-size: 0.95em;
font-family: var(--jp-ui-font-family);
font-weight: bold;
line-height: normal;
user-select: none;
color: white;
padding-left: 2px;
padding-right: 2px;
z-index: 101;
transition: opacity 0.3s ease-in-out;
}
.jp-CodeMirrorEditor .cm-ySelectionInfo {
transition-delay: 0.7s;
opacity: 0;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
opacity: 1;
transition-delay: 0s;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MimeDocument {
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-filebrowser-button-height: 28px;
--jp-private-filebrowser-button-width: 48px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FileBrowser .jp-SidePanel-content {
display: flex;
flex-direction: column;
}
.jp-FileBrowser-toolbar.jp-Toolbar {
flex-wrap: wrap;
row-gap: 12px;
border-bottom: none;
height: auto;
margin: 8px 12px 0;
box-shadow: none;
padding: 0;
justify-content: flex-start;
}
.jp-FileBrowser-Panel {
flex: 1 1 auto;
display: flex;
flex-direction: column;
}
.jp-BreadCrumbs {
flex: 0 0 auto;
margin: 8px 12px;
}
.jp-BreadCrumbs-item {
margin: 0 2px;
padding: 0 2px;
border-radius: var(--jp-border-radius);
cursor: pointer;
}
.jp-BreadCrumbs-item:hover {
background-color: var(--jp-layout-color2);
}
.jp-BreadCrumbs-item:first-child {
margin-left: 0;
}
.jp-BreadCrumbs-item.jp-mod-dropTarget {
background-color: var(--jp-brand-color2);
opacity: 0.7;
}
/*-----------------------------------------------------------------------------
| Buttons
|----------------------------------------------------------------------------*/
.jp-FileBrowser-toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
padding-left: 0;
padding-right: 2px;
align-items: center;
height: unset;
}
.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
width: 40px;
}
/*-----------------------------------------------------------------------------
| Other styles
|----------------------------------------------------------------------------*/
.jp-FileDialog.jp-mod-conflict input {
color: var(--jp-error-color1);
}
.jp-FileDialog .jp-new-name-title {
margin-top: 12px;
}
.jp-LastModified-hidden {
display: none;
}
.jp-FileSize-hidden {
display: none;
}
.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
display: none;
}
/*-----------------------------------------------------------------------------
| DirListing
|----------------------------------------------------------------------------*/
.jp-DirListing {
flex: 1 1 auto;
display: flex;
flex-direction: column;
outline: 0;
}
.jp-DirListing-header {
flex: 0 0 auto;
display: flex;
flex-direction: row;
align-items: center;
overflow: hidden;
border-top: var(--jp-border-width) solid var(--jp-border-color2);
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
}
.jp-DirListing-headerItem {
padding: 4px 12px 2px;
font-weight: 500;
}
.jp-DirListing-headerItem:hover {
background: var(--jp-layout-color2);
}
.jp-DirListing-headerItem.jp-id-name {
flex: 1 0 84px;
}
.jp-DirListing-headerItem.jp-id-modified {
flex: 0 0 112px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-DirListing-headerItem.jp-id-filesize {
flex: 0 0 75px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-id-narrow {
display: none;
flex: 0 0 5px;
padding: 4px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
color: var(--jp-border-color2);
}
.jp-DirListing-narrow .jp-id-narrow {
display: block;
}
.jp-DirListing-narrow .jp-id-modified,
.jp-DirListing-narrow .jp-DirListing-itemModified {
display: none;
}
.jp-DirListing-headerItem.jp-mod-selected {
font-weight: 600;
}
/* increase specificity to override bundled default */
.jp-DirListing-content {
flex: 1 1 auto;
margin: 0;
padding: 0;
list-style-type: none;
overflow: auto;
background-color: var(--jp-layout-color1);
}
.jp-DirListing-content mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
color: var(--jp-ui-inverse-font-color0);
}
/* Style the directory listing content when a user drops a file to upload */
.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
outline: 5px dashed rgba(128, 128, 128, 0.5);
outline-offset: -10px;
cursor: copy;
}
.jp-DirListing-item {
display: flex;
flex-direction: row;
align-items: center;
padding: 4px 12px;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-DirListing-checkboxWrapper {
/* Increases hit area of checkbox. */
padding: 4px;
}
.jp-DirListing-header
.jp-DirListing-checkboxWrapper
+ .jp-DirListing-headerItem {
padding-left: 4px;
}
.jp-DirListing-content .jp-DirListing-checkboxWrapper {
position: relative;
left: -4px;
margin: -4px 0 -4px -8px;
}
.jp-DirListing-checkboxWrapper.jp-mod-visible {
visibility: visible;
}
/* For devices that support hovering, hide checkboxes until hovered, selected...
*/
@media (hover: hover) {
.jp-DirListing-checkboxWrapper {
visibility: hidden;
}
.jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
.jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
visibility: visible;
}
}
.jp-DirListing-item[data-is-dot] {
opacity: 75%;
}
.jp-DirListing-item.jp-mod-selected {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.jp-DirListing-item.jp-mod-dropTarget {
background: var(--jp-brand-color3);
}
.jp-DirListing-item:hover:not(.jp-mod-selected) {
background: var(--jp-layout-color2);
}
.jp-DirListing-itemIcon {
flex: 0 0 20px;
margin-right: 4px;
}
.jp-DirListing-itemText {
flex: 1 0 64px;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
user-select: none;
}
.jp-DirListing-itemText:focus {
outline-width: 2px;
outline-color: var(--jp-inverse-layout-color1);
outline-style: solid;
outline-offset: 1px;
}
.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
outline-color: var(--jp-layout-color1);
}
.jp-DirListing-itemModified {
flex: 0 0 125px;
text-align: right;
}
.jp-DirListing-itemFileSize {
flex: 0 0 90px;
text-align: right;
}
.jp-DirListing-editor {
flex: 1 0 64px;
outline: none;
border: none;
color: var(--jp-ui-font-color1);
background-color: var(--jp-layout-color1);
}
.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
color: var(--jp-success-color1);
content: '\25CF';
font-size: 8px;
position: absolute;
left: -8px;
}
.jp-DirListing-item.jp-mod-running.jp-mod-selected
.jp-DirListing-itemIcon::before {
color: var(--jp-ui-inverse-font-color1);
}
.jp-DirListing-item.lm-mod-drag-image,
.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
font-size: var(--jp-ui-font-size1);
padding-left: 4px;
margin-left: 4px;
width: 160px;
background-color: var(--jp-ui-inverse-font-color2);
box-shadow: var(--jp-elevation-z2);
border-radius: 0;
color: var(--jp-ui-font-color1);
transform: translateX(-40%) translateY(-58%);
}
.jp-Document {
min-width: 120px;
min-height: 120px;
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Main OutputArea
| OutputArea has a list of Outputs
|----------------------------------------------------------------------------*/
.jp-OutputArea {
overflow-y: auto;
}
.jp-OutputArea-child {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-OutputPrompt {
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-outprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
opacity: var(--jp-cell-prompt-opacity);
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-OutputArea-prompt {
display: table-cell;
vertical-align: top;
}
.jp-OutputArea-output {
display: table-cell;
width: 100%;
height: auto;
overflow: auto;
user-select: text;
-moz-user-select: text;
-webkit-user-select: text;
-ms-user-select: text;
}
.jp-OutputArea .jp-RenderedText {
padding-left: 1ch;
}
/**
* Prompt overlay.
*/
.jp-OutputArea-promptOverlay {
position: absolute;
top: 0;
width: var(--jp-cell-prompt-width);
height: 100%;
opacity: 0.5;
}
.jp-OutputArea-promptOverlay:hover {
background: var(--jp-layout-color2);
box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
cursor: zoom-out;
}
.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
cursor: zoom-in;
}
/**
* Isolated output.
*/
.jp-OutputArea-output.jp-mod-isolated {
width: 100%;
display: block;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
position: relative;
}
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/* pre */
.jp-OutputArea-output pre {
border: none;
margin: 0;
padding: 0;
overflow-x: auto;
overflow-y: auto;
word-break: break-all;
word-wrap: break-word;
white-space: pre-wrap;
}
/* tables */
.jp-OutputArea-output.jp-RenderedHTMLCommon table {
margin-left: 0;
margin-right: 0;
}
/* description lists */
.jp-OutputArea-output dl,
.jp-OutputArea-output dt,
.jp-OutputArea-output dd {
display: block;
}
.jp-OutputArea-output dl {
width: 100%;
overflow: hidden;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dt {
font-weight: bold;
float: left;
width: 20%;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dd {
float: left;
width: 80%;
padding: 0;
margin: 0;
}
.jp-TrimmedOutputs pre {
background: var(--jp-layout-color3);
font-size: calc(var(--jp-code-font-size) * 1.4);
text-align: center;
text-transform: uppercase;
}
/* Hide the gutter in case of
* - nested output areas (e.g. in the case of output widgets)
* - mirrored output areas
*/
.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
display: none;
}
/* Hide empty lines in the output area, for instance due to cleared widgets */
.jp-OutputArea-prompt:empty {
padding: 0;
border: 0;
}
/*-----------------------------------------------------------------------------
| executeResult is added to any Output-result for the display of the object
| returned by a cell
|----------------------------------------------------------------------------*/
.jp-OutputArea-output.jp-OutputArea-executeResult {
margin-left: 0;
width: 100%;
}
/* Text output with the Out[] prompt needs a top padding to match the
* alignment of the Out[] prompt itself.
*/
.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
padding-top: var(--jp-code-padding);
border-top: var(--jp-border-width) solid transparent;
}
/*-----------------------------------------------------------------------------
| The Stdin output
|----------------------------------------------------------------------------*/
.jp-Stdin-prompt {
color: var(--jp-content-font-color0);
padding-right: var(--jp-code-padding);
vertical-align: baseline;
flex: 0 0 auto;
}
.jp-Stdin-input {
font-family: var(--jp-code-font-family);
font-size: inherit;
color: inherit;
background-color: inherit;
width: 42%;
min-width: 200px;
/* make sure input baseline aligns with prompt */
vertical-align: baseline;
/* padding + margin = 0.5em between prompt and cursor */
padding: 0 0.25em;
margin: 0 0.25em;
flex: 0 0 70%;
}
.jp-Stdin-input::placeholder {
opacity: 0;
}
.jp-Stdin-input:focus {
box-shadow: none;
}
.jp-Stdin-input:focus::placeholder {
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Output Area View
|----------------------------------------------------------------------------*/
.jp-LinkedOutputView .jp-OutputArea {
height: 100%;
display: block;
}
.jp-LinkedOutputView .jp-OutputArea-output:only-child {
height: 100%;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
@media print {
.jp-OutputArea-child {
break-inside: avoid-page;
}
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-OutputPrompt {
display: table-row;
text-align: left;
}
.jp-OutputArea-child .jp-OutputArea-output {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
}
/* Trimmed outputs warning */
.jp-TrimmedOutputs > a {
margin: 10px;
text-decoration: none;
cursor: pointer;
}
.jp-TrimmedOutputs > a:hover {
text-decoration: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Table of Contents
|----------------------------------------------------------------------------*/
:root {
--jp-private-toc-active-width: 4px;
}
.jp-TableOfContents {
display: flex;
flex-direction: column;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
height: 100%;
}
.jp-TableOfContents-placeholder {
text-align: center;
}
.jp-TableOfContents-placeholderContent {
color: var(--jp-content-font-color2);
padding: 8px;
}
.jp-TableOfContents-placeholderContent > h3 {
margin-bottom: var(--jp-content-heading-margin-bottom);
}
.jp-TableOfContents .jp-SidePanel-content {
overflow-y: auto;
}
.jp-TableOfContents-tree {
margin: 4px;
}
.jp-TableOfContents ol {
list-style-type: none;
}
/* stylelint-disable-next-line selector-max-type */
.jp-TableOfContents li > ol {
/* Align left border with triangle icon center */
padding-left: 11px;
}
.jp-TableOfContents-content {
/* left margin for the active heading indicator */
margin: 0 0 0 var(--jp-private-toc-active-width);
padding: 0;
background-color: var(--jp-layout-color1);
}
.jp-tocItem {
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-tocItem-heading {
display: flex;
cursor: pointer;
}
.jp-tocItem-heading:hover {
background-color: var(--jp-layout-color2);
}
.jp-tocItem-content {
display: block;
padding: 4px 0;
white-space: nowrap;
text-overflow: ellipsis;
overflow-x: hidden;
}
.jp-tocItem-collapser {
height: 20px;
margin: 2px 2px 0;
padding: 0;
background: none;
border: none;
cursor: pointer;
}
.jp-tocItem-collapser:hover {
background-color: var(--jp-layout-color3);
}
/* Active heading indicator */
.jp-tocItem-heading::before {
content: ' ';
background: transparent;
width: var(--jp-private-toc-active-width);
height: 24px;
position: absolute;
left: 0;
border-radius: var(--jp-border-radius);
}
.jp-tocItem-heading.jp-tocItem-active::before {
background-color: var(--jp-brand-color1);
}
.jp-tocItem-heading:hover.jp-tocItem-active::before {
background: var(--jp-brand-color0);
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Collapser {
flex: 0 0 var(--jp-cell-collapser-width);
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
border-radius: var(--jp-border-radius);
opacity: 1;
}
.jp-Collapser-child {
display: block;
width: 100%;
box-sizing: border-box;
/* height: 100% doesn't work because the height of its parent is computed from content */
position: absolute;
top: 0;
bottom: 0;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Hiding collapsers in print mode.
Note: input and output wrappers have "display: block" propery in print mode.
*/
@media print {
.jp-Collapser {
display: none;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Header/Footer
|----------------------------------------------------------------------------*/
/* Hidden by zero height by default */
.jp-CellHeader,
.jp-CellFooter {
height: 0;
width: 100%;
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Input
|----------------------------------------------------------------------------*/
/* All input areas */
.jp-InputArea {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-InputArea-editor {
display: table-cell;
overflow: hidden;
vertical-align: top;
/* This is the non-active, default styling */
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
background: var(--jp-cell-editor-background);
}
.jp-InputPrompt {
display: table-cell;
vertical-align: top;
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-inprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
opacity: var(--jp-cell-prompt-opacity);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-InputArea-editor {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
.jp-InputPrompt {
display: table-row;
text-align: left;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Placeholder {
display: table;
table-layout: fixed;
width: 100%;
}
.jp-Placeholder-prompt {
display: table-cell;
box-sizing: border-box;
}
.jp-Placeholder-content {
display: table-cell;
padding: 4px 6px;
border: 1px solid transparent;
border-radius: 0;
background: none;
box-sizing: border-box;
cursor: pointer;
}
.jp-Placeholder-contentContainer {
display: flex;
}
.jp-Placeholder-content:hover,
.jp-InputPlaceholder > .jp-Placeholder-content:hover {
border-color: var(--jp-layout-color3);
}
.jp-Placeholder-content .jp-MoreHorizIcon {
width: 32px;
height: 16px;
border: 1px solid transparent;
border-radius: var(--jp-border-radius);
}
.jp-Placeholder-content .jp-MoreHorizIcon:hover {
border: 1px solid var(--jp-border-color1);
box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
background-color: var(--jp-layout-color0);
}
.jp-PlaceholderText {
white-space: nowrap;
overflow-x: hidden;
color: var(--jp-inverse-layout-color3);
font-family: var(--jp-code-font-family);
}
.jp-InputPlaceholder > .jp-Placeholder-content {
border-color: var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-cell-scrolling-output-offset: 5px;
}
/*-----------------------------------------------------------------------------
| Cell
|----------------------------------------------------------------------------*/
.jp-Cell {
padding: var(--jp-cell-padding);
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Common input/output
|----------------------------------------------------------------------------*/
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: flex;
flex-direction: row;
padding: 0;
margin: 0;
/* Added to reveal the box-shadow on the input and output collapsers. */
overflow: visible;
}
/* Only input/output areas inside cells */
.jp-Cell-inputArea,
.jp-Cell-outputArea {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Collapser
|----------------------------------------------------------------------------*/
/* Make the output collapser disappear when there is not output, but do so
* in a manner that leaves it in the layout and preserves its width.
*/
.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
border: none !important;
background: transparent !important;
}
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
min-height: var(--jp-cell-collapser-min-height);
}
/*-----------------------------------------------------------------------------
| Output
|----------------------------------------------------------------------------*/
/* Put a space between input and output when there IS output */
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
margin-top: 5px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
overflow-y: auto;
max-height: 24em;
margin-left: var(--jp-private-cell-scrolling-output-offset);
resize: vertical;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
max-height: unset;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
content: ' ';
box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
width: 100%;
height: 100%;
position: sticky;
bottom: 0;
top: 0;
margin-top: -50%;
float: left;
display: block;
pointer-events: none;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
padding-top: 6px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
width: calc(
var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
);
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
}
/*-----------------------------------------------------------------------------
| CodeCell
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| MarkdownCell
|----------------------------------------------------------------------------*/
.jp-MarkdownOutput {
display: table-cell;
width: 100%;
margin-top: 0;
margin-bottom: 0;
padding-left: var(--jp-code-padding);
}
.jp-MarkdownOutput.jp-RenderedHTMLCommon {
overflow: auto;
}
/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
.jp-collapseHeadingButton {
display: flex;
min-height: var(--jp-cell-collapser-min-height);
font-size: var(--jp-code-font-size);
position: absolute;
background-color: transparent;
background-size: 25px;
background-repeat: no-repeat;
background-position-x: center;
background-position-y: top;
background-image: var(--jp-icon-caret-down);
right: 0;
top: 0;
bottom: 0;
}
.jp-collapseHeadingButton.jp-mod-collapsed {
background-image: var(--jp-icon-caret-right);
}
/*
set the container font size to match that of content
so that the nested collapse buttons have the right size
*/
.jp-MarkdownCell .jp-InputPrompt {
font-size: var(--jp-content-font-size1);
}
/*
Align collapseHeadingButton with cell top header
The font sizes are identical to the ones in packages/rendermime/style/base.css
*/
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
font-size: var(--jp-content-font-size5);
background-position-y: calc(0.3 * var(--jp-content-font-size5));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
font-size: var(--jp-content-font-size4);
background-position-y: calc(0.3 * var(--jp-content-font-size4));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
font-size: var(--jp-content-font-size3);
background-position-y: calc(0.3 * var(--jp-content-font-size3));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
font-size: var(--jp-content-font-size2);
background-position-y: calc(0.3 * var(--jp-content-font-size2));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
font-size: var(--jp-content-font-size1);
background-position-y: top;
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
font-size: var(--jp-content-font-size0);
background-position-y: top;
}
/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
display: none;
}
.jp-Notebook.jp-mod-showHiddenCellsButton
:is(.jp-MarkdownCell:hover, .jp-mod-active)
.jp-collapseHeadingButton {
display: flex;
}
/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
margin-top: var(--jp-code-padding);
border: 1px solid var(--jp-border-color2);
background-color: var(--jp-border-color3) !important;
color: var(--jp-content-font-color0) !important;
display: flex;
}
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
background-color: var(--jp-border-color2) !important;
}
.jp-showHiddenCellsButton {
display: none;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Using block instead of flex to allow the use of the break-inside CSS property for
cell outputs.
*/
@media print {
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: block;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-notebook-toolbar-padding: 2px 5px 2px 2px;
}
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-NotebookPanel-toolbar {
padding: var(--jp-notebook-toolbar-padding);
/* disable paint containment from lumino 2.0 default strict CSS containment */
contain: style size !important;
}
.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
border: none;
box-shadow: none;
}
.jp-Notebook-toolbarCellTypeDropdown select {
height: 24px;
font-size: var(--jp-ui-font-size1);
line-height: 14px;
border-radius: 0;
display: block;
}
.jp-Notebook-toolbarCellTypeDropdown span {
top: 5px !important;
}
.jp-Toolbar-responsive-popup {
position: absolute;
height: fit-content;
display: flex;
flex-direction: row;
flex-wrap: wrap;
justify-content: flex-end;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: var(--jp-notebook-toolbar-padding);
z-index: 1;
right: 0;
top: 0;
}
.jp-Toolbar > .jp-Toolbar-responsive-opener {
margin-left: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-Notebook-ExecutionIndicator {
position: relative;
display: inline-block;
height: 100%;
z-index: 9997;
}
.jp-Notebook-ExecutionIndicator-tooltip {
visibility: hidden;
height: auto;
width: max-content;
width: -moz-max-content;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color1);
text-align: justify;
border-radius: 6px;
padding: 0 5px;
position: fixed;
display: table;
}
.jp-Notebook-ExecutionIndicator-tooltip.up {
transform: translateX(-50%) translateY(-100%) translateY(-32px);
}
.jp-Notebook-ExecutionIndicator-tooltip.down {
transform: translateX(calc(-100% + 16px)) translateY(5px);
}
.jp-Notebook-ExecutionIndicator-tooltip.hidden {
display: none;
}
.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
visibility: visible;
}
.jp-Notebook-ExecutionIndicator span {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
color: var(--jp-ui-font-color1);
line-height: 24px;
display: block;
}
.jp-Notebook-ExecutionIndicator-progress-bar {
display: flex;
justify-content: center;
height: 100%;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*
* Execution indicator
*/
.jp-tocItem-content::after {
content: '';
/* Must be identical to form a circle */
width: 12px;
height: 12px;
background: none;
border: none;
position: absolute;
right: 0;
}
.jp-tocItem-content[data-running='0']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background: none;
}
.jp-tocItem-content[data-running='1']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background-color: var(--jp-inverse-layout-color3);
}
.jp-tocItem-content[data-running='0'],
.jp-tocItem-content[data-running='1'] {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Notebook-footer {
height: 27px;
margin-left: calc(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding)
);
width: calc(
100% -
(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding) + var(--jp-cell-padding)
)
);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
color: var(--jp-ui-font-color3);
margin-top: 6px;
background: none;
cursor: pointer;
}
.jp-Notebook-footer:focus {
border-color: var(--jp-cell-editor-active-border-color);
}
/* For devices that support hovering, hide footer until hover */
@media (hover: hover) {
.jp-Notebook-footer {
opacity: 0;
}
.jp-Notebook-footer:focus,
.jp-Notebook-footer:hover {
opacity: 1;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Imports
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-side-by-side-output-size: 1fr;
--jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
--jp-private-notebook-dragImage-width: 304px;
--jp-private-notebook-dragImage-height: 36px;
--jp-private-notebook-selected-color: var(--md-blue-400);
--jp-private-notebook-active-color: var(--md-green-400);
}
/*-----------------------------------------------------------------------------
| Notebook
|----------------------------------------------------------------------------*/
/* stylelint-disable selector-max-class */
.jp-NotebookPanel {
display: block;
height: 100%;
}
.jp-NotebookPanel.jp-Document {
min-width: 240px;
min-height: 120px;
}
.jp-Notebook {
padding: var(--jp-notebook-padding);
outline: none;
overflow: auto;
background: var(--jp-layout-color0);
}
.jp-Notebook.jp-mod-scrollPastEnd::after {
display: block;
content: '';
min-height: var(--jp-notebook-scroll-padding);
}
.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
contain: strict;
}
.jp-Notebook .jp-Cell {
overflow: visible;
}
.jp-Notebook .jp-Cell .jp-InputPrompt {
cursor: move;
}
/*-----------------------------------------------------------------------------
| Notebook state related styling
|
| The notebook and cells each have states, here are the possibilities:
|
| - Notebook
| - Command
| - Edit
| - Cell
| - None
| - Active (only one can be active)
| - Selected (the cells actions are applied to)
| - Multiselected (when multiple selected, the cursor)
| - No outputs
|----------------------------------------------------------------------------*/
/* Command or edit modes */
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
/* cell is active */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
background: var(--jp-brand-color1);
}
/* cell is dirty */
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
color: var(--jp-warn-color1);
}
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
color: var(--jp-warn-color1);
content: '•';
}
.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
background: var(--jp-warn-color1);
}
/* collapser is hovered */
.jp-Notebook .jp-Cell .jp-Collapser:hover {
box-shadow: var(--jp-elevation-z2);
background: var(--jp-brand-color1);
opacity: var(--jp-cell-collapser-not-active-hover-opacity);
}
/* cell is active and collapser is hovered */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
background: var(--jp-brand-color0);
opacity: 1;
}
/* Command mode */
.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
background: var(--jp-notebook-multiselected-color);
}
.jp-Notebook.jp-mod-commandMode
.jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
background: transparent;
}
/* Edit mode */
.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-cell-editor-active-background);
}
/*-----------------------------------------------------------------------------
| Notebook drag and drop
|----------------------------------------------------------------------------*/
.jp-Notebook-cell.jp-mod-dropSource {
opacity: 0.5;
}
.jp-Notebook-cell.jp-mod-dropTarget,
.jp-Notebook.jp-mod-commandMode
.jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
border-top-color: var(--jp-private-notebook-selected-color);
border-top-style: solid;
border-top-width: 2px;
}
.jp-dragImage {
display: block;
flex-direction: row;
width: var(--jp-private-notebook-dragImage-width);
height: var(--jp-private-notebook-dragImage-height);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
overflow: visible;
}
.jp-dragImage-singlePrompt {
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
.jp-dragImage .jp-dragImage-content {
flex: 1 1 auto;
z-index: 2;
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
line-height: var(--jp-code-line-height);
padding: var(--jp-code-padding);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background-color);
color: var(--jp-content-font-color3);
text-align: left;
margin: 4px 4px 4px 0;
}
.jp-dragImage .jp-dragImage-prompt {
flex: 0 0 auto;
min-width: 36px;
color: var(--jp-cell-inprompt-font-color);
padding: var(--jp-code-padding);
padding-left: 12px;
font-family: var(--jp-cell-prompt-font-family);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: 1.9;
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
}
.jp-dragImage-multipleBack {
z-index: -1;
position: absolute;
height: 32px;
width: 300px;
top: 8px;
left: 8px;
background: var(--jp-layout-color2);
border: var(--jp-border-width) solid var(--jp-input-border-color);
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
/*-----------------------------------------------------------------------------
| Cell toolbar
|----------------------------------------------------------------------------*/
.jp-NotebookTools {
display: block;
min-width: var(--jp-sidebar-min-width);
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
overflow: auto;
}
.jp-ActiveCellTool {
padding: 12px 0;
display: flex;
}
.jp-ActiveCellTool-Content {
flex: 1 1 auto;
}
.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
background: var(--jp-cell-editor-background);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
min-height: 29px;
}
.jp-ActiveCellTool .jp-InputPrompt {
min-width: calc(var(--jp-cell-prompt-width) * 0.75);
}
.jp-ActiveCellTool-CellContent > pre {
padding: 5px 4px;
margin: 0;
white-space: normal;
}
.jp-MetadataEditorTool {
flex-direction: column;
padding: 12px 0;
}
.jp-RankedPanel > :not(:first-child) {
margin-top: 12px;
}
.jp-KeySelector select.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: var(--jp-border-width) solid var(--jp-border-color1);
}
.jp-KeySelector label,
.jp-MetadataEditorTool label,
.jp-NumberSetter label {
line-height: 1.4;
}
.jp-NotebookTools .jp-select-wrapper {
margin-top: 4px;
margin-bottom: 0;
}
.jp-NumberSetter input {
width: 100%;
margin-top: 4px;
}
.jp-NotebookTools .jp-Collapse {
margin-top: 16px;
}
/*-----------------------------------------------------------------------------
| Presentation Mode (.jp-mod-presentationMode)
|----------------------------------------------------------------------------*/
.jp-mod-presentationMode .jp-Notebook {
--jp-content-font-size1: var(--jp-content-presentation-font-size1);
--jp-code-font-size: var(--jp-code-presentation-font-size);
}
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
flex: 0 0 110px;
}
/*-----------------------------------------------------------------------------
| Side-by-side Mode (.jp-mod-sideBySide)
|----------------------------------------------------------------------------*/
.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
margin-top: 3em;
margin-bottom: 3em;
margin-left: 5%;
margin-right: 5%;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
display: grid;
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-output-size)
);
grid-template-rows: auto minmax(0, 1fr) auto;
grid-template-areas:
'header header header'
'input handle output'
'footer footer footer';
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-resized-cell)
);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
grid-area: header;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
grid-area: input;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
/* overwrite the default margin (no vertical separation needed in side by side move */
margin-top: 0;
grid-area: output;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
grid-area: footer;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
grid-area: handle;
user-select: none;
display: block;
height: 100%;
cursor: ew-resize;
padding: 0 var(--jp-cell-padding);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
content: '';
display: block;
background: var(--jp-border-color2);
height: 100%;
width: 5px;
}
.jp-mod-sideBySide.jp-Notebook
.jp-CodeCell.jp-mod-resizedCell
.jp-CellResizeHandle::after {
background: var(--jp-border-color0);
}
.jp-CellResizeHandle {
display: none;
}
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Cell-Placeholder {
padding-left: 55px;
}
.jp-Cell-Placeholder-wrapper {
background: #fff;
border: 1px solid;
border-color: #e5e6e9 #dfe0e4 #d0d1d5;
border-radius: 4px;
-webkit-border-radius: 4px;
margin: 10px 15px;
}
.jp-Cell-Placeholder-wrapper-inner {
padding: 15px;
position: relative;
}
.jp-Cell-Placeholder-wrapper-body {
background-repeat: repeat;
background-size: 50% auto;
}
.jp-Cell-Placeholder-wrapper-body div {
background: #f6f7f8;
background-image: -webkit-linear-gradient(
left,
#f6f7f8 0%,
#edeef1 20%,
#f6f7f8 40%,
#f6f7f8 100%
);
background-repeat: no-repeat;
background-size: 800px 104px;
height: 104px;
position: absolute;
right: 15px;
left: 15px;
top: 15px;
}
div.jp-Cell-Placeholder-h1 {
top: 20px;
height: 20px;
left: 15px;
width: 150px;
}
div.jp-Cell-Placeholder-h2 {
left: 15px;
top: 50px;
height: 10px;
width: 100px;
}
div.jp-Cell-Placeholder-content-1,
div.jp-Cell-Placeholder-content-2,
div.jp-Cell-Placeholder-content-3 {
left: 15px;
right: 15px;
height: 10px;
}
div.jp-Cell-Placeholder-content-1 {
top: 100px;
}
div.jp-Cell-Placeholder-content-2 {
top: 120px;
}
div.jp-Cell-Placeholder-content-3 {
top: 140px;
}
</style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.
Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:
* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations
Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/
:root {
/* Elevation
*
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
*
* https://github.com/material-components/material-components-web
* https://material-components-web.appspot.com/elevation.html
*/
--jp-shadow-base-lightness: 0;
--jp-shadow-umbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.2
);
--jp-shadow-penumbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.14
);
--jp-shadow-ambient-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.12
);
--jp-elevation-z0: none;
--jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
0 1px 1px 0 var(--jp-shadow-penumbra-color),
0 1px 3px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
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<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
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<div class="usecase-title"> Impact of employment on housing prices </div>
<div class="usecase-authors"><b>Authored by: Manasa Nagaraja</b></div>
</div>
</div>
</div>
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<div class="usecase-duration"><b>Duration:</b> {90} mins</div>
<div class="usecase-level-skill">
<div class="usecase-level"><b>Level: </b>{Intermediate}</div>
<div class="usecase-skill"><b>Pre-requisite Skills: </b>Python</div>
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</div>
</div>
</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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<h2>Scenario </h2>
<div>In the busy city of Melbourne, urban planners and policymakers are constantly seeking insights into the complex dynamics between employment patterns and housing markets. With the City of Melbourne's Census of Land Use and Employment (CLUE) dataset at their disposal, they start on a mission to delve deep into this relationship.
<p>Armed with the "Jobs per ANZSIC for blocks" dataset, which provides detailed employment data across various industries and geographical areas, the team sets out to uncover correlations with house prices by small area. My aim is clear to understand how fluctuations in employment levels within different ANZSIC industries impact housing prices in Melbourne's diverse neighborhoods.</p>
<p>As I have to analyze the data, patterns begin to emerge and observe that certain industries, such as technology or finance, exhibit a strong influence on nearby housing prices, driving up demand and property values. On the other hand, fluctuations in employment levels within other industries may have a lesser impact or even a negative correlation with house prices.</p>
<p>With these insights, urban planners gain a deeper understanding of the factors shaping Melbourne's housing market. Armed with data-driven evidence, I can modify urban development strategies to contribute to economic growth, ensure housing affordability, and create vibrant, sustainable communities for Melbourne's residents.</p></div>
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<h3>What this use case will teach you ?</h3>
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<p>At the end of this use case you will:</p>
<ul>
<li>I will learn how to merge and clean datasets from different sources,
ensuring data consistency for analysis.</li>
<li>Performing Exploratory Data Analysis I will learn to understand the
structure of the data, identify patterns, and uncover relationships between variables.</li>
<li>I will gain experience in assessing the correlation between
employment trends across various industries and house prices in different areas, using statistical techniques.</li>
<li>This use case will provide an opportunity to apply statistical methods
to analyze the data, such as hypothesis testing and regression analysis, to draw meaningful conclusions.</li>
<li>I am Planning to upskill with machine learning to explore machine learning algorithms to predict house prices based on employment data and other factors, enhancing your understanding of predictive modeling techniques.</li>
<li>By analysing the impact of employment on housing prices, I will gain insights into urban planning considerations and the economic dynamics of melbourne areas.</li>
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<h3>Introduction</h3>
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<p>In this project, I will be analyzing the relationship between employment trends and housing prices within the City of Melbourne. By searching into datasets provided by the City of Melbourne, I aim to understand how fluctuations in employment across different industries impact the housing market. This analysis holds significant implications for urban planning, economic development, within Melbourne. The datasets include information on employment levels categorized by industry (ANZSIC codes) and housing prices by small area.
Employment patterns have a great influence on housing demand and property prices. Changes in employment levels within specific industries can lead to shifts in population demographics, housing preferences, and overall market dynamics. Understanding these correlations is essential for urban planners, and stakeholders to make informed decisions regarding housing affordability, workforce development, and infrastructure planning.</p>
<p>Through this project, I would like to understand the insights of the relation between employment and housing markets. By leveraging data science techniques, statistical analysis, and machine learning algorithms, I aim to identify patterns, trends, and correlations that can inform strategic decision-making processes. Ultimately, my goal is to contribute to the creation of smarter, gain deeper insights into the dynamics of cities, and identify the areas for improvement, and develop innovative solutions to advance inclusive and strong communities.</p>
<h4>Dataset List</h4>
<ul>
<li>Jobs per ANZSIC for blocks</li>
<li>House Prices by Small Area - Sale Year</li>
</ul>
<h4> Contents </h4>
<ul>
<li>Import important Libraries</li>
<li>Connect, Test and Analysis of Datasets</li>
</ul>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Standard</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">json</span>
<span class="kn">import</span> <span class="nn">io</span>
<span class="c1"># Data import</span>
<span class="kn">import</span> <span class="nn">requests</span>
<span class="c1"># Data manipulation</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVR</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span><span class="p">,</span> <span class="n">skew</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">confusion_matrix</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span><span class="p">,</span> <span class="n">Ridge</span><span class="p">,</span> <span class="n">Lasso</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span><span class="p">,</span> <span class="n">RandomizedSearchCV</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">download_and_load_csv</span><span class="p">(</span><span class="n">url</span><span class="p">):</span>
<span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
<span class="n">csv_string</span> <span class="o">=</span> <span class="n">response</span><span class="o">.</span><span class="n">content</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">'utf-8'</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">io</span><span class="o">.</span><span class="n">StringIO</span><span class="p">(</span><span class="n">csv_string</span><span class="p">))</span>
<span class="k">return</span> <span class="n">df</span>
<span class="c1"># API Link</span>
<span class="n">download_link_1</span> <span class="o">=</span> <span class="s1">'https://data.melbourne.vic.gov.au/api/explore/v2.1/catalog/datasets/employment-by-block-by-anzsic/exports/csv?lang=en&timezone=Australia</span><span class="si">%2F</span><span class="s1">Sydney&use_labels=true&delimiter=%2C'</span>
<span class="c1"># Use functions to download and load data</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">download_and_load_csv</span><span class="p">(</span><span class="n">download_link_1</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre><class 'pandas.core.frame.DataFrame'>
RangeIndex: 12394 entries, 0 to 12393
Data columns (total 23 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Census year 12394 non-null int64
1 Block ID 12394 non-null int64
2 CLUE small area 12394 non-null object
3 Accommodation and Food Services 9554 non-null float64
4 Administrative and Support Services 10373 non-null float64
5 Agriculture, Forestry and Fishing 12099 non-null float64
6 Arts and Recreation Services 8465 non-null float64
7 Construction 10319 non-null float64
8 Education and Training 9841 non-null float64
9 Electricity, Gas, Water and Waste Services 9926 non-null float64
10 Financial and Insurance Services 10816 non-null float64
11 Health Care and Social Assistance 9832 non-null float64
12 Information Media and Telecommunications 10096 non-null float64
13 Manufacturing 10082 non-null float64
14 Mining 11968 non-null float64
15 Other Services 9103 non-null float64
16 Professional, Scientific and Technical Services 10515 non-null float64
17 Public Administration and Safety 10542 non-null float64
18 Rental, Hiring and Real Estate Services 10206 non-null float64
19 Retail Trade 9869 non-null float64
20 Transport, Postal and Warehousing 10145 non-null float64
21 Wholesale Trade 9865 non-null float64
22 Total jobs in block 9701 non-null float64
dtypes: float64(20), int64(2), object(1)
memory usage: 2.2+ MB
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">display</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Census year</th>
<th>Block ID</th>
<th>CLUE small area</th>
<th>Accommodation and Food Services</th>
<th>Administrative and Support Services</th>
<th>Agriculture, Forestry and Fishing</th>
<th>Arts and Recreation Services</th>
<th>Construction</th>
<th>Education and Training</th>
<th>Electricity, Gas, Water and Waste Services</th>
<th>...</th>
<th>Manufacturing</th>
<th>Mining</th>
<th>Other Services</th>
<th>Professional, Scientific and Technical Services</th>
<th>Public Administration and Safety</th>
<th>Rental, Hiring and Real Estate Services</th>
<th>Retail Trade</th>
<th>Transport, Postal and Warehousing</th>
<th>Wholesale Trade</th>
<th>Total jobs in block</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>2018</td>
<td>111</td>
<td>West Melbourne (Residential)</td>
<td>NaN</td>
<td>27.0</td>
<td>0.0</td>
<td>NaN</td>
<td>NaN</td>
<td>0.0</td>
<td>0.0</td>
<td>...</td>
<td>0.0</td>
<td>0.0</td>
<td>NaN</td>
<td>44.0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>0.0</td>
<td>NaN</td>
<td>283.0</td>
</tr>
<tr>
<th>1</th>
<td>2018</td>
<td>114</td>
<td>Melbourne (CBD)</td>
<td>186.0</td>
<td>0.0</td>
<td>0.0</td>
<td>NaN</td>
<td>30.0</td>
<td>NaN</td>
<td>0.0</td>
<td>...</td>
<td>0.0</td>
<td>0.0</td>
<td>NaN</td>
<td>55.0</td>
<td>NaN</td>
<td>0.0</td>
<td>60.0</td>
<td>0.0</td>
<td>0.0</td>
<td>368.0</td>
</tr>
<tr>
<th>2</th>
<td>2018</td>
<td>202</td>
<td>Carlton</td>
<td>NaN</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>...</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>NaN</td>
</tr>
<tr>
<th>3</th>
<td>2018</td>
<td>203</td>
<td>Carlton</td>
<td>4.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>...</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>NaN</td>
<td>0.0</td>
<td>0.0</td>
<td>30.0</td>
</tr>
<tr>
<th>4</th>
<td>2018</td>
<td>204</td>
<td>Carlton</td>
<td>37.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>114.0</td>
<td>0.0</td>
<td>...</td>
<td>0.0</td>
<td>0.0</td>
<td>NaN</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>155.0</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>12389</th>
<td>2018</td>
<td>87</td>
<td>Melbourne (CBD)</td>
<td>211.0</td>
<td>NaN</td>
<td>0.0</td>
<td>NaN</td>
<td>NaN</td>
<td>54.0</td>
<td>NaN</td>
<td>...</td>
<td>NaN</td>
<td>0.0</td>
<td>17.0</td>
<td>848.0</td>
<td>0.0</td>
<td>26.0</td>
<td>0.0</td>
<td>0.0</td>
<td>NaN</td>
<td>5447.0</td>
</tr>
<tr>
<th>12390</th>
<td>2018</td>
<td>88</td>
<td>Melbourne (CBD)</td>
<td>NaN</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>...</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>NaN</td>
<td>NaN</td>
<td>0.0</td>
<td>0.0</td>
<td>46.0</td>
</tr>
<tr>
<th>12391</th>
<td>2018</td>
<td>92</td>
<td>West Melbourne (Residential)</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>12.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>...</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>12.0</td>
</tr>
<tr>
<th>12392</th>
<td>2018</td>
<td>93</td>
<td>Melbourne (CBD)</td>
<td>115.0</td>
<td>NaN</td>
<td>0.0</td>
<td>NaN</td>
<td>0.0</td>
<td>36.0</td>
<td>0.0</td>
<td>...</td>
<td>0.0</td>
<td>0.0</td>
<td>148.0</td>
<td>508.0</td>
<td>NaN</td>
<td>44.0</td>
<td>NaN</td>
<td>0.0</td>
<td>0.0</td>
<td>2168.0</td>
</tr>
<tr>
<th>12393</th>
<td>2018</td>
<td>104</td>
<td>Melbourne (CBD)</td>
<td>167.0</td>
<td>NaN</td>
<td>0.0</td>
<td>0.0</td>
<td>NaN</td>
<td>47.0</td>
<td>NaN</td>
<td>...</td>
<td>NaN</td>
<td>0.0</td>
<td>21.0</td>
<td>32.0</td>
<td>0.0</td>
<td>21.0</td>
<td>19.0</td>
<td>0.0</td>
<td>0.0</td>
<td>380.0</td>
</tr>
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<p>12394 rows × 23 columns</p>
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</div>
</div>
</div>
</div>
</div>
</div>
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">"Block ID"</span><span class="p">,</span><span class="s2">"Accommodation and Food Services"</span><span class="p">,</span><span class="s2">"Administrative and Support Services"</span><span class="p">,</span><span class="s2">"Agriculture, Forestry and Fishing"</span><span class="p">,</span><span class="s2">"Arts and Recreation Services"</span><span class="p">,</span>
<span class="s2">"Construction"</span><span class="p">,</span><span class="s2">"Education and Training"</span><span class="p">,</span><span class="s2">"Electricity, Gas, Water and Waste Services"</span><span class="p">,</span><span class="s2">"Financial and Insurance Services"</span><span class="p">,</span><span class="s2">"Health Care and Social Assistance"</span><span class="p">,</span>
<span class="s2">"Information Media and Telecommunications"</span><span class="p">,</span><span class="s2">"Manufacturing"</span><span class="p">,</span><span class="s2">"Mining"</span><span class="p">,</span><span class="s2">"Other Services"</span><span class="p">,</span><span class="s2">"Professional, Scientific and Technical Services"</span><span class="p">,</span><span class="s2">"Public Administration and Safety"</span><span class="p">,</span>
<span class="s2">"Rental, Hiring and Real Estate Services"</span><span class="p">,</span><span class="s2">"Retail Trade"</span><span class="p">,</span><span class="s2">"Transport, Postal and Warehousing"</span><span class="p">,</span><span class="s2">"Wholesale Trade"</span>
<span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">df3</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="c1"># Convert 'Census year' column to datetime format</span>
<span class="n">df3</span><span class="p">[</span><span class="s1">'Census year'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df3</span><span class="p">[</span><span class="s1">'Census year'</span><span class="p">],</span> <span class="nb">format</span><span class="o">=</span><span class="s1">'%Y'</span><span class="p">)</span>
<span class="c1"># Filter data to keep only years till 2016</span>
<span class="n">df_filtered</span> <span class="o">=</span> <span class="n">df3</span><span class="p">[</span><span class="n">df3</span><span class="p">[</span><span class="s1">'Census year'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">year</span> <span class="o"><=</span> <span class="mi">2016</span><span class="p">]</span>
<span class="c1"># Print the filtered DataFrame</span>
<span class="n">display</span><span class="p">(</span><span class="n">df_filtered</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="jp-Cell-outputWrapper">
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<div class="colab-df-container" id="df-079adf64-85a0-4746-a768-57a1db6906ca">
<div>
<style scoped="">
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Census year</th>
<th>CLUE small area</th>
<th>Total jobs in block</th>
</tr>
</thead>
<tbody>
<tr>
<th>383</th>
<td>2016-01-01</td>
<td>Melbourne (CBD)</td>
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<tr>
<th>384</th>
<td>2016-01-01</td>
<td>Melbourne (CBD)</td>
<td>4608.0</td>
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<tr>
<th>385</th>
<td>2016-01-01</td>
<td>Melbourne (CBD)</td>
<td>2000.0</td>
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<tr>
<th>386</th>
<td>2016-01-01</td>
<td>Melbourne (CBD)</td>
<td>2205.0</td>
</tr>
<tr>
<th>387</th>
<td>2016-01-01</td>
<td>Melbourne (CBD)</td>
<td>5733.0</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>11577</th>
<td>2002-01-01</td>
<td>West Melbourne (Industrial)</td>
<td>55.0</td>
</tr>
<tr>
<th>11578</th>
<td>2014-01-01</td>
<td>City of Melbourne (total)</td>
<td>447074.0</td>
</tr>
<tr>
<th>11579</th>
<td>2012-01-01</td>
<td>City of Melbourne (total)</td>
<td>436436.0</td>
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<tr>
<th>11580</th>
<td>2009-01-01</td>
<td>City of Melbourne (total)</td>
<td>415487.0</td>
</tr>
<tr>
<th>11581</th>
<td>2002-01-01</td>
<td>City of Melbourne (total)</td>
<td>319729.0</td>
</tr>
</tbody>
</table>
<p>8757 rows × 3 columns</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df5</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">df_filtered</span><span class="p">)</span>
<span class="c1"># Filter data to keep only rows where 'CLUE small area' is neither 'City of Melbourne' nor 'West Melbourne (Residential)'</span>
<span class="n">df_filtered2</span> <span class="o">=</span> <span class="n">df5</span><span class="p">[</span><span class="o">~</span><span class="n">df5</span><span class="p">[</span><span class="s1">'CLUE small area'</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="s1">'City of Melbourne (total)'</span><span class="p">,</span> <span class="s1">'West Melbourne (Industrial)'</span><span class="p">])]</span>
<span class="c1"># Print the filtered DataFrame</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_filtered2</span><span class="p">)</span>
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<pre> Census year CLUE small area Total jobs in block
383 2016-01-01 Melbourne (CBD) 1045.0
384 2016-01-01 Melbourne (CBD) 4608.0
385 2016-01-01 Melbourne (CBD) 2000.0
386 2016-01-01 Melbourne (CBD) 2205.0
387 2016-01-01 Melbourne (CBD) 5733.0
... ... ... ...
11570 2002-01-01 Parkville 4426.0
11573 2002-01-01 Kensington 3.0
11574 2002-01-01 Docklands 0.0
11575 2002-01-01 Docklands 1825.0
11576 2002-01-01 Docklands NaN
[8547 rows x 3 columns]
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Rename columns</span>
<span class="n">melbourne_job</span> <span class="o">=</span> <span class="n">df_filtered2</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s2">"Census year"</span><span class="p">:</span> <span class="s2">"Year"</span><span class="p">,</span>
<span class="s2">"CLUE small area"</span><span class="p">:</span> <span class="s2">"Region"</span><span class="p">})</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">display</span><span class="p">(</span><span class="n">melbourne_job</span><span class="p">)</span>
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<p>8547 rows × 3 columns</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">melbourne_job</span><span class="o">.</span><span class="n">shape</span>
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<pre>(8547, 3)</pre>
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<p>Checking and Handling missing values.</p>
<h3>Jobs per ANZSIC for blocks</h3>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Check for missing values in the melbourne_job DataFrame</span>
<span class="n">melbourne_job</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
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<pre>Year 0
Region 0
Total jobs in block 1905
dtype: int64</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Remove rows with missing values from the melbourne_job DataFrame</span>
<span class="n">melbourne_job</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">inplace</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">melbourne_job</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
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<pre>Year 0
Region 0
Total jobs in block 0
dtype: int64</pre>
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<p>The above code performs several data processing and cleaning steps on a dataset Jobs per ANZSIC for blocks and by importing necessary libraries and download_and_load_csv function s then used to download data from a specific API link related to employment data in Melbourne.After downloading the data, several columns representing different sectors of employment are dropped using the drop method.The 'Census year' column is converted to datetime format using pd.to_datetime and Data is filtered to keep only rows with census years until 2016 using boolean indexing.Another filter is applied to exclude rows where the 'CLUE small area' is either 'City of Melbourne (total)' or 'West Melbourne Industrial and the range and meaning of values in different columns such as 'Census year', 'Block ID', 'CLUE small area', and the employment sectors.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># data set name</span>
<span class="n">download_link_2</span> <span class="o">=</span> <span class="s1">'https://data.melbourne.vic.gov.au/api/explore/v2.1/catalog/datasets/house-prices-by-small-area-sale-year/exports/csv?lang=en&timezone=Australia</span><span class="si">%2F</span><span class="s1">Sydney&use_labels=true&delimiter=%2C'</span>
<span class="n">df1</span> <span class="o">=</span> <span class="n">download_and_load_csv</span><span class="p">(</span><span class="n">download_link_2</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df7</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">df1</span><span class="p">)</span>
<span class="n">df_filtered4</span> <span class="o">=</span> <span class="n">df7</span><span class="p">[</span><span class="n">df7</span><span class="p">[</span><span class="s1">'Small_Area'</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">'NA'</span><span class="p">]</span>
<span class="c1"># Print the resulting DataFrame</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_filtered4</span><span class="p">)</span>
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<pre> Sale_Year Small_Area Type Median_Price \
0 2000 Carlton House/Townhouse 316250.0
1 2000 Carlton Residential Apartment 220000.0
2 2000 East Melbourne House/Townhouse 622500.0
3 2000 East Melbourne Residential Apartment 295000.0
4 2000 Kensington House/Townhouse 215250.0
.. ... ... ... ...
342 2016 North Melbourne Residential Apartment 515000.0
343 2016 Parkville House/Townhouse 1761250.0
344 2016 Parkville Residential Apartment 500000.0
345 2016 South Yarra House/Townhouse 2135000.0
346 2016 Southbank Residential Apartment 565000.0
Transaction_Count
0 116
1 309
2 42
3 139
4 258
.. ...
342 231
343 32
344 63
345 34
346 701
[347 rows x 5 columns]
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_filtered4</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre><class 'pandas.core.frame.DataFrame'>
RangeIndex: 347 entries, 0 to 346
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Sale_Year 347 non-null int64
1 Small_Area 340 non-null object
2 Type 347 non-null object
3 Median_Price 319 non-null float64
4 Transaction_Count 347 non-null int64
dtypes: float64(1), int64(2), object(2)
memory usage: 13.7+ KB
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_filtered4</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<th></th>
<th>Sale_Year</th>
<th>Small_Area</th>
<th>Type</th>
<th>Median_Price</th>
<th>Transaction_Count</th>
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<td>Carlton</td>
<td>House/Townhouse</td>
<td>316250.0</td>
<td>116</td>
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<td>2000</td>
<td>Carlton</td>
<td>Residential Apartment</td>
<td>220000.0</td>
<td>309</td>
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<td>2000</td>
<td>East Melbourne</td>
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<td>622500.0</td>
<td>42</td>
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<th>3</th>
<td>2000</td>
<td>East Melbourne</td>
<td>Residential Apartment</td>
<td>295000.0</td>
<td>139</td>
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<th>4</th>
<td>2000</td>
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<p>Checking and Handling missing values.</p>
<h3> for House Prices by Small Area - Sale Year dataset</h3>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Check for missing values in the df_filtered4 DataFrame</span>
<span class="n">df_filtered4</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">any</span><span class="p">()</span>
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<pre>Sale_Year False
Small_Area True
Type False
Median_Price True
Transaction_Count False
dtype: bool</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Check for missing values in the df_filtered4 DataFrame</span>
<span class="n">df_filtered4</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
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<pre>Sale_Year 0
Small_Area 7
Type 0
Median_Price 28
Transaction_Count 0
dtype: int64</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Remove rows with missing values from the df_filtered4 DataFrame</span>
<span class="n">df_filtered4</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">inplace</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_filtered4</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
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<pre>Sale_Year 0
Small_Area 0
Type 0
Median_Price 0
Transaction_Count 0
dtype: int64</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_filtered4</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">any</span><span class="p">()</span>
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<pre>Sale_Year False
Small_Area False
Type False
Median_Price False
Transaction_Count False
dtype: bool</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Rename columns</span>
<span class="n">houseprice_melbourne</span> <span class="o">=</span> <span class="n">df_filtered4</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s2">"Sale_Year"</span><span class="p">:</span> <span class="s2">"Year"</span><span class="p">,</span>
<span class="s2">"Small_Area"</span><span class="p">:</span> <span class="s2">"Region"</span><span class="p">,</span><span class="s2">"Type"</span><span class="p">:</span><span class="s2">"House_type"</span><span class="p">,</span><span class="s2">"Median_price"</span><span class="p">:</span><span class="s2">"House_price"</span><span class="p">,})</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Check duplicate rows</span>
<span class="n">houseprice_melbourne</span><span class="p">[</span><span class="n">houseprice_melbourne</span><span class="o">.</span><span class="n">duplicated</span><span class="p">()]</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">houseprice_melbourne</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<p>The above code performs several data processing and cleaning steps on a dataset House Prices by Small Area - Sale Year and by importing necessary libraries and download_and_load_csv function s then used to download data from a specific API link related to house prices by small area in Melbourne.After craeting a dataframe we filter out rows where the 'Small_Area' column is not equal to 'NA' using boolean indexing and the Missing values are checked, dropped, and checked again, Columns are renamed to improve clarity and consistency and The code checks for duplicate rows in the DataFrame based on all columns and if any duplicates are found, they are dropped from the DataFrame.</p>
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<p>(EDA) - Exploratory Data Analysis for Jobs per ANZSIC for blocks and House Prices by Small Area - Sale Year</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">melbourne_job</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre><class 'pandas.core.frame.DataFrame'>
Index: 6642 entries, 383 to 11575
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Year 6642 non-null datetime64[ns]
1 Region 6642 non-null object
2 Total jobs in block 6642 non-null float64
dtypes: datetime64[ns](1), float64(1), object(1)
memory usage: 207.6+ KB
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<p>Now lets analyze the distribution and outliers in the "Total jobs in block" column of the melbourne_job DataFrame.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1">#Add headers before each part/chart so that the user can follow easier</span>
<span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">melbourne_job</span><span class="p">[</span><span class="s2">"Total jobs in block"</span><span class="p">],</span> <span class="n">fit</span><span class="o">=</span><span class="n">norm</span><span class="p">)</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">prob</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">melbourne_job</span><span class="p">[</span><span class="s2">"Total jobs in block"</span><span class="p">],</span> <span class="n">plot</span><span class="o">=</span><span class="n">plt</span><span class="p">)</span>
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<pre><ipython-input-27-1c23928f2ceb>:2: UserWarning:
`distplot` is a deprecated function and will be removed in seaborn v0.14.0.
Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).
For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
sns.distplot(melbourne_job["Total jobs in block"], fit=norm)
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<p>For the analysis performed the visualization of the distribution of a dataset called "Total jobs in block" along with a probability plot.</p>
<ul>
<li>The distribution plot, created with the function sns.distplot, shows the probability density of the data. The x-axis represents the number of jobs in a block, while the y-axis represents the density. The curve depicts a bell-shaped distribution, which suggests that the data is normally distributed. The line superimposed on the curve is the probability density function (PDF) of a normal distribution that has been fitted to the data. This line helps to assess how well the normal distribution fits the data.</li>
<li>The probability plot, created with the function stats.probplot, shows how well the data follows a normal distribution. The x-axis represents the ordered values from the data set, while the y-axis represents the expected cumulative probability values for a normal distribution and in the distribution the points deviate slightly from a straight line, which shows that the data may not be perfectly normal.</li>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [28]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Value < Q1 - 1,5*IQR OR Value > Q3 + 1,5 * IQR</span>
<span class="k">def</span> <span class="nf">finding_outliers</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">variable_name</span><span class="p">)</span> <span class="p">:</span>
<span class="n">iqr</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">variable_name</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span> <span class="o">-</span> <span class="n">data</span><span class="p">[</span><span class="n">variable_name</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span>
<span class="n">lower</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">variable_name</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span> <span class="o">-</span><span class="mf">1.5</span><span class="o">*</span><span class="n">iqr</span>
<span class="n">upper</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">variable_name</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.5</span><span class="o">*</span><span class="n">iqr</span>
<span class="k">return</span> <span class="n">data</span> <span class="p">[(</span><span class="n">data</span><span class="p">[</span><span class="n">variable_name</span><span class="p">]</span> <span class="o"><</span> <span class="n">lower</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">variable_name</span><span class="p">]</span> <span class="o">></span> <span class="n">upper</span><span class="p">)]</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [29]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">,</span> <span class="s2">"Boxplot"</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">transAxes</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">"Total jobs in block"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">melbourne_job</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [30]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Function to find outliers</span>
<span class="k">def</span> <span class="nf">finding_outliers</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">variable_name</span><span class="p">):</span>
<span class="n">iqr</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">variable_name</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span> <span class="o">-</span> <span class="n">data</span><span class="p">[</span><span class="n">variable_name</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span>
<span class="n">lower</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">variable_name</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.5</span> <span class="o">*</span> <span class="n">iqr</span>
<span class="n">upper</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">variable_name</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.5</span> <span class="o">*</span> <span class="n">iqr</span>
<span class="k">return</span> <span class="n">data</span><span class="p">[(</span><span class="n">data</span><span class="p">[</span><span class="n">variable_name</span><span class="p">]</span> <span class="o"><</span> <span class="n">lower</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">variable_name</span><span class="p">]</span> <span class="o">></span> <span class="n">upper</span><span class="p">)]</span>
<span class="c1"># Price boxplot</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">,</span> <span class="s2">"Price Boxplot"</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">transAxes</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">"Total jobs in block"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">melbourne_job</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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w7kpj0AlwVCMgDAZfTo0Tp48KA2bdqkt99+220uLi6O7d8AXDa8utwiPT1dt912m6Kjo2WxWLRixQq3eYvFUubrueeec9VcddVVpeaffvppt+NkZWXp5ptvVmhoqGJiYvTss8+W6iU1NVWtWrVSaGio2rVrp/fff79SzhkAqrP09HRt3rxZXbt21R133KF+/frpjjvuUNeuXbV582alp6d7u0UAqBJevZJcUFCg6667TsOHD9ef/vSnUvOHDx92e//BBx8oOTlZd9xxh9v4k08+qZEjR7re161b1/Wzw+FQz5491aNHD6WkpOjLL7/U8OHDVa9ePY0aNUqStHnzZg0aNEizZ89Wv3799MYbb6h///7asWOH2rZt68lTBoBqy+l0asGCBYqNjdVf//pXBQT8ch2luLhY06dP18KFCxUXF6fAwEAvdgoAlc+rIbl3797q3bv3BecjIyPd3qelpenWW29Vs2bN3Mbr1q1bqrbE0qVLVVhYqFdffVXBwcG69tprlZmZqeeff94VkufNm6devXpp6tSpkqS//OUvWrdunf7+978rJSXlt5wiAPiMrKws2e12PfbYY24BWZICAgKUlJSksWPHKisrSx07dvRSlwBQNXxmd4sjR45ozZo1Sk5OLjX39NNPq379+urYsaOee+45t7uyMzIyFB8f73ajSUJCgvbu3asTJ064anr06OF2zISEhAtugyRJZ8+elcPhcHsBgC/Lzc2VJDVt2rTM+ZLxkjoA8Gc+E5L/+c9/qm7duqWWZYwfP17Lli3Tp59+qvvuu09PPfWUHnroIde83W5XRESE22dK3tvt9ovWlMyXZfbs2bLZbK5XTEzMbzo/APC28PBwSVJ2dnaZ8yXjJXUA4M98JiS/+uqrSkpKUmhoqNv4pEmT1K1bN7Vv316jR4/WnDlz9NJLL+ns2bOV2s+0adOUn5/veh08eLBSfx8AVLb27dsrMjJSS5cuVXFxsdtccXGxli5dqqioKLVv395LHQJA1fGJkPyf//xHe/fu1YgRIy5Z26VLFxUVFWn//v2Sfl7XfOTIEbeakvcl65gvVHOhdc6SFBISIqvV6vYCAF8WGBio+++/XxkZGZo+fbp2796tU6dOaffu3Zo+fboyMjI0ZswYbtoDcFnwiZC8ePFiderUSdddd90lazMzMxUQEKBGjRpJkmJjY5Wenq5z5865atatW6eWLVsqLCzMVbN+/Xq346xbt06xsbEePAsAqP7i4+M1c+ZM7du3T2PHjlWfPn00duxYZWdna+bMmYqPj/d2iwBQJby6u8XJkyf17bffut5nZ2crMzNT4eHhatKkiaSft3BLTU3VnDlzSn0+IyNDn332mW699VbVrVtXGRkZmjhxou6++25XAB48eLBmzpyp5ORkPfzww9q1a5fmzZunuXPnuo7z4IMP6pZbbtGcOXPUt29fLVu2TNu2bdOiRYsq+RsAgOonPj5ecXFxysrKUm5ursLDw9W+fXuuIAO4rFgMwzC89cs3bNigW2+9tdT40KFDtWTJEknSokWLNGHCBB0+fFg2m82tbseOHbr//vu1Z88enT17Vk2bNtU999yjSZMmKSQkxFWXlZWlsWPH6vPPP1eDBg30wAMP6OGHH3Y7VmpqqqZPn679+/frmmuu0bPPPqs+ffqU+1wcDodsNpvy8/NZegEAAFANVSSveTUk+xNCMgAAQPVWkbzmE2uSAQAAgKpESAYAAABMCMkAAACACSEZAAAAMCEkAwAAACaEZAAAAMCEkAwAAACYEJIBAAAAE0IyAAAAYEJIBgAAAEwIyQAAAIAJIRkAAAAwISQDAAAAJoRkAAAAwISQDAAAAJgQkgEAAAATQjIAAABgQkgGAAAATAjJAAAAgAkhGQAAADAhJAMAAAAmhGQAAADAhJAMAAAAmBCSAQAAABNCMgAAAGBCSAYAAABMCMkAAACACSEZAAAAMCEkAwAAACaEZAAAAMCEkAwAAACYEJIBAAAAE0IyAAAAYEJIBgAAAEwIyQAAAIAJIRkAAAAwISQDAAAAJoRkAAAAwISQDAAAAJgQkgEAAAATQjIAAABgQkgGAAAATAjJAAAAgAkhGQAAADAhJAMAAAAmhGQAAADAhJAMAAAAmBCSAQAAABNCMgAAAGBCSAYAAABMCMkAAACACSEZAAAAMCEkAwAAACaEZAAAAMCEkAwAAACYEJIBAAAAE0IyAAAAYEJIBgAAAEwIyQAAAIAJIRkAAAAwISQDAAAAJoRkAAAAwISQDAAAAJgQkgEAAAATQjIAAABgQkgGAAAATAjJAAAAgAkhGQAAADDxakhOT0/XbbfdpujoaFksFq1YscJt/t5775XFYnF79erVy60mNzdXSUlJslqtqlevnpKTk3Xy5Em3mqysLN18880KDQ1VTEyMnn322VK9pKamqlWrVgoNDVW7du30/vvve/x8AQAA4Bu8GpILCgp03XXXaf78+Res6dWrlw4fPux6vfnmm27zSUlJ2r17t9atW6fVq1crPT1do0aNcs07HA717NlTV155pbZv367nnntOM2bM0KJFi1w1mzdv1qBBg5ScnKydO3eqf//+6t+/v3bt2uX5kwYAAEC1ZzEMw/B2E5JksVj03nvvqX///q6xe++9V3l5eaWuMJf4+uuv1aZNG33++efq3LmzJGnt2rXq06ePDh06pOjoaC1cuFCPPvqo7Ha7goODJUmPPPKIVqxYoT179kiS7rrrLhUUFGj16tWuY3ft2lUdOnRQSkpKufp3OByy2WzKz8+X1Wr9Fd8AAAAAKlNF8lq1X5O8YcMGNWrUSC1bttSYMWP0448/uuYyMjJUr149V0CWpB49eiggIECfffaZqyY+Pt4VkCUpISFBe/fu1YkTJ1w1PXr0cPu9CQkJysjIuGBfZ8+elcPhcHsBAADAP1TrkNyrVy/961//0vr16/XMM89o48aN6t27t5xOpyTJbrerUaNGbp8JCgpSeHi47Ha7qyYiIsKtpuT9pWpK5ssye/Zs2Ww21ysmJua3nSwAAACqjSBvN3AxAwcOdP3crl07tW/fXs2bN9eGDRvUvXt3L3YmTZs2TZMmTXK9dzgcBGUAAAA/Ua2vJJs1a9ZMDRo00LfffitJioyM1NGjR91qioqKlJubq8jISFfNkSNH3GpK3l+qpmS+LCEhIbJarW4vAAAA+AefCsmHDh3Sjz/+qKioKElSbGys8vLytH37dlfNJ598ouLiYnXp0sVVk56ernPnzrlq1q1bp5YtWyosLMxVs379erfftW7dOsXGxlb2KQEAAKAa8mpIPnnypDIzM5WZmSlJys7OVmZmpg4cOKCTJ09q6tSp2rJli/bv36/169crMTFRV199tRISEiRJrVu3Vq9evTRy5Eht3bpVmzZt0rhx4zRw4EBFR0dLkgYPHqzg4GAlJydr9+7deuuttzRv3jy3pRIPPvig1q5dqzlz5mjPnj2aMWOGtm3bpnHjxlX5dwIAAIBqwPCiTz/91JBU6jV06FDj1KlTRs+ePY2GDRsaNWrUMK688kpj5MiRht1udzvGjz/+aAwaNMioU6eOYbVajWHDhhk//fSTW80XX3xh3HTTTUZISIhxxRVXGE8//XSpXt5++22jRYsWRnBwsHHttdcaa9asqdC55OfnG5KM/Pz8in8RAAAAqHQVyWvVZp9kX8c+yQAAANWbX+2TDAAAAFQ1QjIAAABgQkgGAAAATAjJAAAAgAkhGQAAADAhJAMAAAAmhGQAAADAhJAMAAAAmBCSAQAAABNCMgAAAGBCSAYAAABMCMkAAACACSEZAAAAMCEkAwAAACaEZAAAAMCEkAwAAACYEJIBAAAAE0IyAAAAYEJIBgAAAEwIyQAAAIAJIRkAAAAwISQDAAAAJoRkAAAAwCTI2w0AAKqfwsJCpaWlKScnR9HR0UpMTFRwcLC32wKAKkNIBgC4SUlJUWpqqpxOp9vYgAEDNHr0aC92BgBVh5AMAHBJSUnRsmXLFBYWpuTkZMXGxiojI0OLFy/WsmXLJImgDOCyYDEMw/B2E/7A4XDIZrMpPz9fVqvV2+0AQIUVFhaqd+/eslqtSk1NVVDQL9dRioqKNGDAADkcDn3wwQcsvQDgkyqS17hxDwAgSUpLS5PT6VRycrJbQJakoKAgDR8+XE6nU2lpaV7qEACqDiEZACBJysnJkSTFxsaWOV8yXlIHAP6MkAwAkCRFR0dLkjIyMsqcLxkvqQMAf0ZIBgBIkhITExUYGKjFixerqKjIba6oqEivvvqqAgMDlZiY6KUOAaDqsLsFAECSFBwcrAEDBmjZsmW688471bNnT0VHRysnJ0cfffSR8vLyNHDgQG7aA3BZICQDAFxGjx6tgwcPatOmTXr77bfd5uLi4tj+DcBlg5AMAHBJT0/X5s2b1bVrV11xxRUqLCxUcHCwfvjhB23evFnp6emKj4/3dpsAUOkIyQAASZLT6dSCBQsUGxurv/71rwoI+OW2leLiYk2fPl0LFy5UXFycAgMDvdgpAFQ+btwDAEiSsrKyZLfblZSU5BaQJSkgIEBJSUk6fPiwsrKyvNQhAFQdQjIAQJKUm5srSWratGmZ8yXjJXUA4M8IyQAASVJ4eLgkKTs7u8z5kvGSOgDwZ4RkAIAkqX379oqMjNTSpUtVXFzsNldcXKylS5cqKipK7du391KHAFB1CMkAAElSYGCg7r//fmVkZGj69OnavXu3Tp06pd27d2v69OnKyMjQmDFjuGkPwGXBYhiG4e0m/IHD4ZDNZlN+fr6sVqu32wGAXy09PV0LFiyQ3W53jUVFRWnMmDFs/wbAp1UkrxGSPYSQDMCfOJ1OZWVlKTc3V+Hh4Wrfvj1XkAH4vIrkNfZJBgCUEhgYqI4dO3q7DQDwGtYkAwAAACaEZAAAAMCEkAwAAACYEJIBAAAAE0IyAAAAYEJIBgAAAEwIyQAAAIAJIRkAAAAwISQDAAAAJoRkAAAAwISQDAAAAJgQkgEAAAATQjIAAABgQkgGAAAATAjJAAAAgAkhGQAAADAhJAMAAAAmQd5uAABQ/Zw+fVovv/yyDh06pMaNG+u+++5TzZo1vd0WAFQZi2EYhreb8AcOh0M2m035+fmyWq3ebgcAfrVHH31UmzZtKjUeFxenWbNmeaEjAPCMiuQ1llsAAFxKAnKNGjU0ePBgvf766xo8eLBq1KihTZs26dFHH/V2iwBQJbiS7CFcSQbg606fPq3evXurRo0aWrNmjYKDg11zhYWF6tu3r86dO6cPPviApRcAfBJXkgEAFfbyyy9LkgYMGOAWkCUpODhYd955p1sdAPgzQjIAQJJ06NAhSVKfPn3KnC8ZL6kDAH9GSAYASJIaN24sSXr//ffLnC8ZL6kDAH9GSAYASJLuu+8+SVJqaqoKCwvd5goLC7V8+XK3OgDwZ14Nyenp6brtttsUHR0ti8WiFStWuObOnTunhx9+WO3atVPt2rUVHR2tIUOGKCcnx+0YV111lSwWi9vr6aefdqvJysrSzTffrNDQUMXExOjZZ58t1UtqaqpatWql0NBQtWvX7oJXUgDAX9WsWVNxcXE6d+6c+vbtq5dfflkHDx7Uyy+/7LppLy4ujpv2AFwWvBqSCwoKdN1112n+/Pml5k6dOqUdO3boscce044dO/Tuu+9q7969uv3220vVPvnkkzp8+LDr9cADD7jmHA6HevbsqSuvvFLbt2/Xc889pxkzZmjRokWums2bN2vQoEFKTk7Wzp071b9/f/Xv31+7du2qnBMHgGpq1qxZrqD85ptv6p577tGbb77pCsjskwzgclFttoCzWCx677331L9//wvWfP7557rxxhv1/fffq0mTJpJ+vpI8YcIETZgwoczPLFy4UI8++qjsdrvrbu1HHnlEK1as0J49eyRJd911lwoKCrR69WrX57p27aoOHTooJSWlXP2zBRwAf8IT9wD4I7/dAi4/P18Wi0X16tVzG3/66adVv359dezYUc8995yKiopccxkZGYqPj3fbzighIUF79+7ViRMnXDU9evRwO2ZCQoIyMjIu2MvZs2flcDjcXgDgL2rWrKkJEybob3/7myZMmEBABnDZCfJ2A+V15swZPfzwwxo0aJBb8h8/fryuv/56hYeHa/PmzZo2bZoOHz6s559/XpJkt9vVtGlTt2NFRES45sLCwmS3211j59fY7fYL9jN79mzNnDnTU6cHANVKYWGh0tLSlJOTo+joaCUmJpbaOxkA/JlPhORz587pz3/+swzD0MKFC93mJk2a5Pq5ffv2Cg4O1n333afZs2crJCSk0nqaNm2a2+92OByKiYmptN8HAFUlJSVFqampcjqdbmMDBgzQ6NGjvdgZAFSdah+SSwLy999/r08++eSS60e6dOmioqIi7d+/Xy1btlRkZKSOHDniVlPyPjIy0vWfZdWUzJclJCSkUkM4AHhDSkqKli1bprCwMCUnJys2NlYZGRlavHixli1bJkkEZQCXhWq9JrkkIP/vf//Txx9/rPr161/yM5mZmQoICFCjRo0kSbGxsUpPT9e5c+dcNevWrVPLli0VFhbmqlm/fr3bcdatW6fY2FgPng0AVG+FhYVKTU1VWFiYUlNT1a9fP9WvX1/9+vVzGzfvoQwA/sirIfnkyZPKzMxUZmamJCk7O1uZmZk6cOCAzp07pzvvvFPbtm3T0qVL5XQ6ZbfbZbfbXX9AZ2Rk6IUXXtAXX3yhffv2aenSpZo4caLuvvtuVwAePHiwgoODlZycrN27d+utt97SvHnz3JZKPPjgg1q7dq3mzJmjPXv2aMaMGdq2bZvGjRtX5d8JAHhLWlqanE6nkpOTFRTk/g+NQUFBGj58uJxOp9LS0rzUIQBUHa8ut9i2bZtuvfVW1/uS4Dp06FDNmDFDK1eulCR16NDB7XOffvqpunXrppCQEC1btkwzZszQ2bNn1bRpU02cONEtANtsNn300UcaO3asOnXqpAYNGujxxx/XqFGjXDW/+93v9MYbb2j69On6v//7P11zzTVasWKF2rZtW4lnDwDVS8nDmmJjY8u8ca/kX9fMD3UCAH9UbfZJ9nXskwzA16Wmpmr+/Pnq3Lmzdu7c6XbjXmBgoDp27Kht27Zp7NixGjBggBc7BYBfx2/3SQYAVJ7ExERZLBZt27ZNVqtVU6ZM0TvvvKMpU6bIarVq27ZtslgsSkxM9HarAFDpCMkAgFIMwyj1AoDLSbXfAg4AUDXS0tJkGIZuuOEG7dixQ3PmzHHNBQYGqnPnztq2bZvS0tJYbgHA7xGSAQCSfrkh75FHHlHdunVL3bjncDh05513cuMegMsCIRkAIEmKjo6W9PP2mv369St1tTgjI8OtDgD8GWuSAQCSfr5xLzAwUIsXL1ZRUZHbXFFRkV599VUFBgZy4x6AywIhGQAgSQoODtaAAQN04sQJDRgwQKtWrdLx48e1atUqt/Hg4GBvtwoAlY7lFgAAl9GjR0v6ec9k8417AwcOdM0DgL/jYSIewsNEAPiTsp64xxVkAL6uInmNK8kAgFJKll4AwOWKNckAAACACSEZAAAAMCEkAwAAACaEZAAAAMCEkAwAAACYEJIBAAAAE0IyAAAAYEJIBgAAAEwIyQAAAIAJIRkAAAAwISQDAAAAJoRkAAAAwISQDAAAAJgQkgEAAAATQjIAAABgQkgGAAAATAjJAAAAgAkhGQAAADAhJAMAAAAmhGQAAADAJMjbDQAAqh+n06msrCzl5uYqPDxc7du3V2BgoLfbAoAqQ0gGALhJT0/XggULZLfbXWORkZG6//77FR8f78XOAKDqsNwCAOCSnp6uJ554Qs2aNdP8+fP1/vvva/78+WrWrJmeeOIJpaene7tFAKgSFsMwDG834Q8cDodsNpvy8/NltVq93Q4AVJjT6VRSUpKaNWumv/71rwoI+OU6SnFxsaZPn67s7Gy9/vrrLL0A4JMqkte4kgwAkCRlZWXJbrcrKSnJLSBLUkBAgJKSknT48GFlZWV5qUMAqDqEZACAJCk3N1eS1LRp0zLnS8ZL6gDAn3HjHgBAkhQeHi5Jys7O1jXXXKO0tDTl5OQoOjpaiYmJys7OdqsDAH/GmmQPYU0yAF9XsiY5ICBAdrtdxcXFrrmAgABFRkbKMAzWJAPwWaxJBgBUWGBgoJo3b66cnBy3gCz9fONeTk6OmjVrRkAGcFkgJAMAJEmFhYXKyMi4aE1GRoYKCwurqCMA8B5CMgBAkvTee++5riAHBQWpe/fuuv/++9W9e3cFBf18C0txcbHee+89b7YJAFWCkAwAkCRlZmZK+nnZxYoVK9SqVSsdPnxYrVq10ooVK1zLLErqAMCfsbsFAECSdPDgQUlS/fr1dfvtt7utS164cKEaNGigo0ePuuoAwJ8RkgEAkqSaNWtKko4ePVpqrri42DVeUgcA/ozlFgAASVLLli09WgcAvoyQDACQJEVERHi0DgB8WYVD8q5duy44t2LFit/SCwDAi7Zs2eLROgDwZRUOyQkJCa5Hk57vnXfeUVJSkkeaAgBUvR9//FHSzzfulaXkcdQldQDgzyockkeMGKEePXrIbre7xt566y0NGTJES5Ys8WRvAIAqVBKOT506pVWrVql///7q3Lmz+vfvr1WrVun06dNudQDgzyockmfOnKk+ffqoR48eys3N1RtvvKFhw4bpX//6lwYMGFAZPQIAqsBNN90kSTp9+rSGDBmi5s2b65FHHlHz5s01ZMgQV0guqQMAf2YxDMP4NR9MSkrS559/rh9++EFvvPGGEhMTPd2bT3E4HLLZbMrPz5fVavV2OwBQYYWFhUpISNDF/lqwWCz68MMPFRwcXIWdAYBnVCSvlWuf5JUrV5Ya+9Of/qT//Oc/GjRokCwWi6vm9ttv/xUtAwC8LTg4WHfddZeWLVt2wZq77rqLgAzgslCuK8kBAeVblWGxWOR0On9zU76IK8kA/MXo0aO1Z8+eUuOtWrVSSkqKFzoCAM/w+JXk8x9NCgDwXykpKdqzZ4/CwsJ03XXXqWbNmjp9+rS++OIL7dmzRykpKRo9erS32wSASsdjqQEAkn5ek5yamqqwsDClpqYqKOiXvyKKioo0YMAApaamavjw4Sy5AOD3Kry7xfjx4/Xiiy+WGv/73/+uCRMmeKInAIAXpKWlyel0Kjk52S0gS1JQUJCGDx8up9OptLQ0L3UIAFWnwiH5nXfeUVxcXKnx3/3ud1q+fLlHmgIAVL2cnBxJUmxsbJnzJeMldQDgzyockn/88UfZbLZS41arVcePH/dIUwCAqhcdHS1JysjIkNPp1M6dO7V+/Xrt3LlTTqdTGRkZbnUA4M8qvCb56quv1tq1azVu3Di38Q8++EDNmjXzWGMAgKqVmJiolJQULVy4UP/617909OhR11yjRo1UUFCgwMDAy35ffACXhwqH5EmTJmncuHE6duyYfv/730uS1q9frzlz5uiFF17wdH8AgCoSHBysrl27atOmTSosLNSgQYPUp08fvf/++1q+fLnOnTunuLg4btoDcFn4VU/cW7hwoWbNmuVal3bVVVdpxowZGjJkiMcb9BXskwzA1zmdTiUlJSkgIEBHjhxx2/c+MDBQERERMgxDr7/+ugIDA73YKQD8Oh7fJ9lszJgxGjNmjI4dO6aaNWuqTp06v6pRAED1kZWVJbvdrvnz5+uaa65RWlqacnJyFB0drcTERP3vf//T2LFjlZWVpY4dO3q7XQCoVL96n+Rjx45p7969kn5+ClODBg081hQAoOrl5uZKkpo2barg4GANGDDAbb5p06ZudQDgzyq8u0VBQYGGDx+uqKgoxcfHKz4+XlFRUUpOTtapU6cqo0cAQBUIDw+XJGVnZ5c5XzJeUgcA/qzCIXnSpEnauHGjVq1apby8POXl5SktLU0bN27U5MmTK6NHAEAVaN++vSIjI7V06VIVFxe7zRUXF2vp0qWKiopS+/btvdQhAFSdCt+416BBAy1fvlzdunVzG//000/15z//WceOHfNkfz6DG/cA+IP09HQ98cQTio2NVVJSkpo2bars7GwtXbpUGRkZmjlzpuLj473dJgD8KpV6496pU6cUERFRarxRo0YstwAAHxcfH6+ZM2dqwYIFGjt2rGs8KiqKgAzgslLh5RaxsbF64okndObMGdfY6dOnNXPmzAs+yvRC0tPTddtttyk6OloWi0UrVqxwmzcMQ48//riioqJUs2ZN9ejRQ//73//canJzc5WUlCSr1ap69eopOTlZJ0+edKvJysrSzTffrNDQUMXExOjZZ58t1UtqaqpatWql0NBQtWvXTu+//36FzgUA/EV8fLyWLl2quXPn6rHHHtPcuXP1+uuvE5ABXFYqHJLnzZunTZs2qXHjxurevbu6d++umJgYbd68WfPmzavQsQoKCnTddddp/vz5Zc4/++yzevHFF5WSkqLPPvtMtWvXVkJCgltAT0pK0u7du7Vu3TqtXr1a6enpGjVqlGve4XCoZ8+euvLKK7V9+3Y999xzmjFjhhYtWuSq2bx5swYNGqTk5GTt3LlT/fv3V//+/bVr164KfjsA4B8CAwPVsWNHde/eXR07dmRfZACXnV/1MJFTp05p6dKl2rNnjySpdevWSkpKUs2aNX99IxaL3nvvPfXv31/Sz1eRo6OjNXnyZE2ZMkWSlJ+fr4iICC1ZskQDBw7U119/rTZt2ujzzz9X586dJUlr165Vnz59dOjQIUVHR2vhwoV69NFHZbfbXU+JeuSRR7RixQpX/3fddZcKCgq0evVqVz9du3ZVhw4dlJKSUq7+WZMMAABQvVX6w0Rq1aqlkSNH/qrmyis7O1t2u109evRwjdlsNnXp0kUZGRkaOHCgMjIyVK9ePVdAlqQePXooICBAn332mf74xz8qIyND8fHxbo9RTUhI0DPPPKMTJ04oLCxMGRkZmjRpktvvT0hIKLX843xnz57V2bNnXe8dDocHzhoAqgen06msrCzl5uYqPDxc7du352oygMtKuULyypUry33A22+//Vc3cz673S5JpW4SjIiIcM3Z7XY1atTIbT4oKEjh4eFuNSUb4J9/jJK5sLAw2e32i/6essyePVszZ878FWcGANVbenq6FixY4PZnYGRkpO6//37WJQO4bJQrJJcsgbgUi8Uip9P5W/rxGdOmTXO7+uxwOBQTE+PFjgDgtyvZAq5Lly6Ki4tTYWGhgoOD9cMPP+iJJ55ghwsAl41yhWTzpvJVITIyUpJ05MgRRUVFucaPHDmiDh06uGqOHj3q9rmioiLl5ua6Ph8ZGakjR4641ZS8v1RNyXxZQkJCFBIS8ivODACqJ6fTqQULFigqKkpbt251+7M/ICBAUVFRWrhwoeLi4lh6AcDvVXh3i6rStGlTRUZGav369a4xh8Ohzz77zLXVXGxsrPLy8rR9+3ZXzSeffKLi4mJ16dLFVZOenq5z5865atatW6eWLVsqLCzMVXP+7ympqeiWdgDgy7KysmS325WTkyObzaYpU6bonXfe0ZQpU2Sz2ZSTk6PDhw8rKyvL260CQKX7VSF5/fr16tevn5o3b67mzZurX79++vjjjyt8nJMnTyozM1OZmZmSfr5ZLzMzUwcOHJDFYtGECRP017/+VStXrtSXX36pIUOGKDo62rX8o3Xr1urVq5dGjhyprVu3atOmTRo3bpwGDhyo6OhoSdLgwYMVHBys5ORk7d69W2+99ZbmzZvntlTiwQcf1Nq1azVnzhzt2bNHM2bM0LZt2zRu3Lhf8/UAgE8q+Ze5evXqKTU1Vf369VP9+vXVr18/paamql69em51AODPKhySFyxYoF69eqlu3bp68MEH9eCDD8pqtapPnz4X3O/4QrZt26aOHTuqY8eOkqRJkyapY8eOevzxxyVJDz30kB544AGNGjVKN9xwg06ePKm1a9cqNDTUdYylS5eqVatW6t69u/r06aObbrrJbQ9km82mjz76SNnZ2erUqZMmT56sxx9/3G0v5d/97nd64403tGjRIl133XVavny5VqxYobZt21b06wEAn/X1119Lkvr06aOgIPfVeEFBQerdu7dbHQD4swrvk9y4cWM98sgjpa6yzp8/X0899ZR++OEHjzboK9gnGYCve+GFF7RixQp17txZzz77rAICfrmOUlxcrIceekjbtm1T//79NWHCBO81CgC/UkXyWoWvJOfl5alXr16lxnv27Kn8/PyKHg4AUE1cccUVkn7+V77p06dr9+7dOnXqlHbv3q3p06dr27ZtbnUA4M8qHJJvv/12vffee6XG09LS1K9fP480BQCoeomJiQoMDFTt2rX13XffaezYserTp4/Gjh2rffv2qXbt2goMDFRiYqK3WwWASleuLeBefPFF189t2rTRrFmztGHDBtfuD1u2bNGmTZs0efLkyukSAFDpgoODNWDAAC1btkzBwcH685//rKioKB0+fFjr1q1TQUGBBg4c6PYEUwDwV+Vak2x+Yt0FD2axaN++fb+5KV/EmmQA/iIlJUWpqaluD4cKDAzUgAEDNHr0aC92BgC/TUXyWoVv3EPZCMkA/ElhYaHS0tKUk5Oj6OhoJSYmcgUZgM+rSF4r13ILAMDlpWTpBQBcrqrtE/cAAAAAb+FKMgCglNOnT+vll1/WoUOH1LhxY913332qWbOmt9sCgCrDmmQPYU0yAH/x6KOPatOmTaXG4+LiNGvWLC90BACeUakPEwEA+K8LBWRJ2rRpkx599NEq7ggAvONXLbfIy8vT1q1bdfToURUXF7vNDRkyxCONAQCq1unTp10BuV69ehoxYoRiY2OVkZGhV155RXl5edq0aZNOnz7N0gsAfq/Cyy1WrVqlpKQknTx5UlarVRaL5ZeDWSzKzc31eJO+gOUWAHzdnDlztGrVKoWGhmr16tUKCvrlOkpRUZH69u2rs2fP6rbbbuPhUQB8UqUut5g8ebKGDx+ukydPKi8vTydOnHC9LteADAD+IDMzU5KUlJSk4uJipaamat68eUpNTVVxcbEGDx7sVgcA/qzCyy1++OEHjR8/XrVq1aqMfgAAXlJy5Xjjxo167bXX3JbTLVy40PX01fOvMAOAv6rwleSEhARt27atMnoBAHjRH/7wB0nSt99+W+p+k+LiYn333XdudQDgzyp8OaBv376aOnWqvvrqK7Vr1041atRwm7/99ts91hwAoOrcfvvtWrRoUbnqAMDfVTgkjxw5UpL05JNPlpqzWCxyOp2/vSsAQJVbuXJluetK1icDgL+q8HKL4uLiC74IyADguz766CNJUmBgYJnzJeMldQDgz7j7AgAgSTp58qQkyel0KiwsTNddd51q1qyp06dP64svvtCJEyfc6gDAn5UrJL/44osaNWqUQkND9eKLL160dvz48R5pDABQtRo3bqzjx49Lkt58802Fhoa65s6cOaNevXq56gDA35UrJM+dO1dJSUkKDQ3V3LlzL1hnsVgIyQDgoxo3buzaA3ngwIFKTk52PXFv8eLFbnUA4O/KFZKzs7PL/BkA4D/O3/84Ly9Pc+bMuWQdAPirCt+4BwDwT1dccYVH6wDAlxGSAQCSpMTERAUGBqp27dqqX7++21yDBg1Uu3ZtBQYGKjEx0UsdAkDVISQDACRJwcHBGjBggAoKClRcXKxu3bqpV69e6tatm5xOpwoKCjRgwAAFBwd7u1UAqHQsLAMAuIwePVoHDx7Upk2btGHDBre5uLg4jR492juNAUAVIyQDAFzS09O1efNmde3aVVdccYUKCwsVHBysH374QZs3b1Z6erri4+O93SYAVLoKh+S1a9eqTp06uummmyRJ8+fP1z/+8Q+1adNG8+fPV1hYmMebBABUPqfTqQULFig2NlZ//etfFRDwy4q84uJiTZ8+XQsXLlRcXNwFn8oHAP6iwmuSp06dKofDIUn68ssvNXnyZPXp00fZ2dmaNGmSxxsEAFSNrKws2e12JSUlyTAM7dy5U+vXr9fOnTtlGIaSkpJ0+PBhZWVlebtVAKh0Fb6SnJ2drTZt2kiS3nnnHfXr109PPfWUduzYoT59+ni8QQBA1cjNzZUk5eTk6C9/+YvsdrtrLjIyUsnJyW51AODPKnwlOTg4WKdOnZIkffzxx+rZs6ckKTw83HWFGQDge8LDwyVJs2bNUrNmzTR//ny9//77mj9/vpo1a6ZZs2a51QGAP6vwleSbbrpJkyZNUlxcnLZu3aq33npLkvTNN9/wqFIA8GHXXnutAgMDZbVaNW3aNC1evFiHDh1S48aNNW3aNA0ZMkQOh0PXXnutt1sFgEpX4ZD897//Xffff7+WL1+uhQsXup689MEHH6hXr14ebxAAUDV2794tp9OpEydO6LbbbnONb9u2TStWrHCr69ixoxc6BICqU+GQ3KRJE61evbrU+Ny5cz3SEADAO8q71pg1yQAuB79qn2Sn06n33ntPX3/9tSSpdevW6t+/v4KC2HYZAHxVnTp1JEmBgYFKS0vTBx98oJycHEVHR6t3795KTEyU0+l01QGAP6twqt29e7duu+02HTlyRC1btpQkPfPMM2rYsKFWrVqltm3berxJAEDl27RpkyTJarVq2LBhOnbsmGvu7bffltVq1YkTJ7Rp0yZ16dLFW20CQJWocEgeMWKE2rZtq+3bt7seHHLixAnde++9GjVqlDZv3uzxJgEAlS8nJ0fSz3+mm50fmEvqAMCfVTgkZ2Zmatu2bW5P1gsLC9OsWbN0ww03eLQ5AEDViY6O9mgdAPiyCofkFi1a6MiRI6W2ADp69KiuvvpqjzUGAKhaN9xwg1auXClJSktL0759+5Sbm6vw8HA1a9ZMiYmJrjoA8HflepiIw+FwvWbPnq3x48dr+fLlOnTokA4dOqTly5drwoQJeuaZZyq7XwBAJTl/m7c777xTW7duVYsWLbR161bdeeedZdYBgL8q15XkevXqyWKxuN4bhqE///nPrjHDMCRJt912m5xOZyW0CQCobCdPnpQktWzZUnv37tWbb76pN9980zXfokULffPNN646APBn5QrJn376aWX3AQDwspJwfPLkSa1Zs0b/+Mc/XE/cGzlypEaOHOmqAwB/ZzFKLgPjN3E4HLLZbMrPz5fVavV2OwBQYadPn1bv3r0lSV27dtU999yjpk2bKjs7W//+97+1ZcsWST8/YbVmzZrebBUAfpWK5LVf9fSPvLw8LV682PUwkWuvvVbDhw+XzWb7NYcDAFQDNWvWVFxcnDZt2qQtW7a4QvH54uLiCMgALgvlunHvfNu2bVPz5s01d+5c5ebmKjc3V88//7yaN2+uHTt2VEaPAIAqMmvWLMXFxZU5FxcXp1mzZlVxRwDgHRUOyRMnTtTtt9+u/fv3691339W7776r7Oxs9evXTxMmTKiEFgEAVSkhIUENGzZ0G2vYsKESEhK81BEAVL0Kr0muWbOmdu7cqVatWrmNf/XVV+rcubNOnTrl0QZ9BWuSAfiD9PR0Pf744xecf/LJJxUfH1+FHQGA51Qkr1X4SrLVatWBAwdKjR88eFB169at6OEAANWE0+nU008/fdGaZ555hq0+AVwWKhyS77rrLiUnJ+utt97SwYMHdfDgQS1btkwjRozQoEGDKqNHAEAV2LFjxyX/NbCgoID7TwBcFiq8u8Xf/vY3WSwWDRkyREVFRZKkGjVqaMyYMZe8AgEAqL7Wrl1b7joeTQ3A31X4SnJwcLDmzZunEydOKDMzU5mZmcrNzdXcuXMVEhJSGT0CAKrAt99+6/a+TZs2mjNnjtq0aXPROgDwR79qn2RJqlWrltq1a+fJXgAAXlTyr4OStHLlStdNLZ06dZLD4dDtt99eqg4A/FW5QvKf/vQnLVmyRFarVX/6058uWlunTh1de+21Gj16NA8XAQAfcuLECdfPTz31VKkn7pVVBwD+qlwh2WazyWKxuH6+mLNnzyolJUWbNm3SypUrf3uHAIAqcf6OoBd64p65DgD8VblC8muvvVbmzxfy1VdfcVMHAPiYiIgI7d+/v1x1AODvKnzjXnm0bNlSmzdvroxDAwAqyfPPP+/ROgDwZZUSkgMDA3XddddVxqEBAJWkTp06Hq0DAF9WKSEZAOB73n33XY/WAYAvIyQDACRJ//nPfzxaBwC+jJAMAJAkHT9+3KN1AODLyrW7RUW2civZbB4A4FvOnDnj0ToA8GXlCsn9+/cv18EsFoucTudv6QcA4CXl3f+YfZIBXA7KFZKLi4sruw8AgJcFBgZ6tA4AfBlrkgEAktgCDgDOV64ryWYFBQXauHGjDhw4oMLCQre58ePHe6QxAEDVCgsL08GDB8tVBwD+rsIheefOnerTp49OnTqlgoIChYeH6/jx46pVq5YaNWpESAYAH8XuFgDwiwovt5g4caJuu+02nThxQjVr1tSWLVv0/fffq1OnTvrb3/5WGT0CAKqAw+HwaB0A+LIKh+TMzExNnjxZAQEBCgwM1NmzZxUTE6Nnn31W//d//1cZPQIAqkB5b9LmZm4Al4MKh+QaNWooIODnjzVq1EgHDhyQJNlstnKtZauoq666ShaLpdRr7NixkqRu3bqVmhs9erTbMQ4cOKC+ffu6loRMnTpVRUVFbjUbNmzQ9ddfr5CQEF199dVasmSJx88FAKozbtwDgF9UeE1yx44d9fnnn+uaa67RLbfcoscff1zHjx/Xv//9b7Vt29bjDX7++eduey/v2rVLf/jDHzRgwADX2MiRI/Xkk0+63teqVcv1s9PpVN++fRUZGanNmzfr8OHDGjJkiGrUqKGnnnpKkpSdna2+fftq9OjRWrp0qdavX68RI0YoKipKCQkJHj8nAKiOGjZsqKNHj5arDgD8XYWvJD/11FOKioqSJM2aNUthYWEaM2aMjh07ppdfftnjDTZs2FCRkZGu1+rVq9W8eXPdcsstrppatWq51VitVtfcRx99pK+++kqvv/66OnTooN69e+svf/mL5s+f79qZIyUlRU2bNtWcOXPUunVrjRs3Tnfeeafmzp3r8fMBgOrqd7/7nUfrAMCXVTgkd+7cWbfeequkn5dbrF27Vg6HQ9u3b1eHDh083Z+bwsJCvf766xo+fLgsFotrfOnSpWrQoIHatm2radOm6dSpU665jIwMtWvXThEREa6xhIQEORwO7d6921XTo0cPt9+VkJCgjIyMC/Zy9uxZORwOtxcA+DKeuAcAv6hwSP7973+vvLy8UuMOh0O///3vPdHTBa1YsUJ5eXm69957XWODBw/W66+/rk8//VTTpk3Tv//9b919992uebvd7haQJbne2+32i9Y4HA6dPn26zF5mz54tm83mesXExHjiFAHAazZv3uzROgDwZRVek7xhw4ZSDxCRpDNnzug///mPR5q6kMWLF6t3796Kjo52jY0aNcr1c7t27RQVFaXu3bvru+++U/PmzSutl2nTpmnSpEmu9w6Hg6AMwKd9//33kuS6Cfr8XSwCAgJc70vqAMCflTskZ2VluX7+6quvXFdhpZ9vjlu7dq2uuOIKz3Z3nu+//14ff/yx3n333YvWdenSRZL07bffqnnz5oqMjNTWrVvdao4cOSJJioyMdP1nydj5NVarVTVr1izz94SEhCgkJORXnQsAVEfn3yRt3uatuLhYFotFhmG41QGAvyp3SO7QoYPr6kJZyypq1qypl156yaPNne+1115To0aN1Ldv34vWZWZmSpLr5sLY2FjNmjVLR48eVaNGjSRJ69atk9VqVZs2bVw177//vttx1q1bp9jYWA+fBQBUXzabTWfOnHGtOW7RooWio6OVk5Ojb775xjVus9m82SYAVIlyh+Ts7GwZhqFmzZpp69atblsABQcHq1GjRgoMDKyUJouLi/Xaa69p6NChCgr6peXvvvtOb7zxhvr06aP69esrKytLEydOVHx8vNq3by9J6tmzp9q0aaN77rlHzz77rOx2u6ZPn66xY8e6rgSPHj1af//73/XQQw9p+PDh+uSTT/T2229rzZo1lXI+AFAd9enTR6+99prr/TfffKNvvvmmzDoA8HflDslXXnmlJO88aenjjz/WgQMHNHz4cLfx4OBgffzxx3rhhRdUUFCgmJgY3XHHHZo+fbqrJjAwUKtXr9aYMWMUGxur2rVra+jQoW77Kjdt2lRr1qzRxIkTNW/ePDVu3FivvPIKeyQDuKwEBwd7tA4AfFmFb9yTfr6C+8ILL+jrr7+WJLVp00YPPvhgpd0o17NnzzK3HIqJidHGjRsv+fkrr7yy1HIKs27dumnnzp2/ukcA8HXn32viiToA8GUV3gLuww8/VJs2bbR161a1b99e7du312effaZrr71W69atq4weAQBV4Pjx4x6tAwBfVuEryY888ogmTpyop59+utT4ww8/rD/84Q8eaw4AUHXq1avn+tlms6ljx44KDQ3VmTNntHPnTuXn55eqAwB/VeEryV9//bWSk5NLjQ8fPlxfffWVR5oCAFS9/fv3u352Op06ceKEcnJydOLECbdt386vAwB/VeEryQ0bNlRmZqauueYat/HMzEzXFmsAAN9TcqVYkk6ePKkvvvjiknUA4K/KHZKffPJJTZkyRSNHjtSoUaO0b98+/e53v5Mkbdq0Sc8884zbE+gAAL7l/C02PVEHAL7MYpS1bUQZAgMDdfjwYTVs2FAvvPCC5syZo5ycHElSdHS0pk6dqvHjx8tisVRqw9WVw+GQzWZTfn6+rFart9sBgAr75z//6bZP8oUMGzZMQ4cOrYKOAMCzKpLXyn05oCRLWywWTZw4URMnTtRPP/0kSapbt+5vaBcAUB0cO3bMo3UA4Msq9G9m5qvEhGMA8B//+9//PFoHAL6sQiG5RYsWl1xOkZub+5saAgB4R+3atT1aBwC+rEIheebMmbLZbJXVCwDAizp16qQdO3aUqw4A/F2FQvLAgQPZ5g0A/FStWrU8WgcAvqzcDxO5XHetAIDLxdKlSz1aBwC+rNwhuZw7xQEAfNTx48c9WgcAvqzcyy2Ki4srsw8AAACg2ij3lWQAgH8LDAz0aB0A+DJCMgBAklSvXj2392FhYerfv7/CwsIuWgcA/qhCu1sAAPxXQUGB2/sTJ05oxYoVl6wDAH/ElWQAgCSpqKjIo3UA4MsIyQAASVKdOnU8WgcAvoyQDACQJPXt29ejdQDgywjJAABJUl5enkfrAMCXEZIBAJKkzMxMj9YBgC8jJAMAJEmnTp3yaB0A+DJCMgBAklSrVi2P1gGALyMkAwAkSadPn/ZoHQD4MkIyAEASyy0A4HyEZACAJOnMmTMerQMAX0ZIBgAAAEwIyQAAAIAJIRkAIEkKCwvzaB0A+DJCMgBAkhQUFOTROgDwZYRkAIAktoADgPMRkgEAkqQaNWp4tA4AfBkhGQAgSWrVqpVH6wDAlxGSAQCSpNtuu82jdQDgywjJAABJ0okTJzxaBwC+jJAMAJAkrV692qN1AODLCMkAAEnSvn37XD8HBLj/9XD++/PrAMBfEZIBAJKkwsJC189Wq1WTJ0/W8uXLNXnyZFmt1jLrAMBfsSM8AECSZLFYZBiGJCkwMFBz5sxxzTVo0MCtDgD8HVeSAQCSpHr16rl+/vHHH93mjh8/XmYdAPgrQjIAQJLUvXt3j9YBgC8jJAMAJEnXX3+9R+sAwJcRkgEAkqT09HSP1gGALyMkAwAkSZs2bfJoHQD4MkIyAECSVFBQ4NE6APBlhGQAgKTSDxD5rXUA4Mv4kw4AIKn8+x+zTzKAywEhGQAgqfxP0uOJewAuB4RkAAAAwISQDAAAAJgQkgEAkqTQ0FCP1gGALyMkAwAkceMeAJyPkAwAkCQFBQV5tA4AfBkhGQAgiSvJAHA+QjIAQJJUVFTk0ToA8GWEZACAJMnpdHq0DgB8GSEZACBJqlGjhkfrAMCXEZIBAJKk6Ohoj9YBgC8jJAMAJElnzpzxaB0A+DJCMgBAknTs2DGP1gGALyMkAwAkSadPn/ZoHQD4MkIyAAAAYEJIBgAAAEwIyQAASVLNmjU9WgcAvoyQDACQJLVp08ajdQDgywjJAABJ0uHDhz1aBwC+jJAMAJAk5eTkeLQOAHxZtQ7JM2bMkMVicXu1atXKNX/mzBmNHTtW9evXV506dXTHHXfoyJEjbsc4cOCA+vbtq1q1aqlRo0aaOnWqioqK3Go2bNig66+/XiEhIbr66qu1ZMmSqjg9AAAAVFPVOiRL0rXXXqvDhw+7Xv/9739dcxMnTtSqVauUmpqqjRs3KicnR3/6059c806nU3379lVhYaE2b96sf/7zn1qyZIkef/xxV012drb69u2rW2+9VZmZmZowYYJGjBihDz/8sErPEwAAANVHkLcbuJSgoCBFRkaWGs/Pz9fixYv1xhtv6Pe//70k6bXXXlPr1q21ZcsWde3aVR999JG++uorffzxx4qIiFCHDh30l7/8RQ8//LBmzJih4OBgpaSkqGnTppozZ44kqXXr1vrvf/+ruXPnKiEhoUrPFQC8KSgoqNS/tF2oDgD8XbW/kvy///1P0dHRatasmZKSknTgwAFJ0vbt23Xu3Dn16NHDVduqVSs1adJEGRkZkqSMjAy1a9dOERERrpqEhAQ5HA7t3r3bVXP+MUpqSo5xIWfPnpXD4XB7AYAvK09ArkgdAPiyah2Su3TpoiVLlmjt2rVauHChsrOzdfPNN+unn36S3W5XcHCw6tWr5/aZiIgI2e12SZLdbncLyCXzJXMXq3E4HBd99Ors2bNls9lcr5iYmN96ugAAAKgmqvW/mfXu3dv1c/v27dWlSxddeeWVevvtt72+mf20adM0adIk13uHw0FQBgAA8BPV+kqyWb169dSiRQt9++23ioyMVGFhofLy8txqjhw54lrDHBkZWWq3i5L3l6qxWq0XDeIhISGyWq1uLwAAAPgHnwrJJ0+e1HfffaeoqCh16tRJNWrU0Pr1613ze/fu1YEDBxQbGytJio2N1ZdffqmjR4+6atatWyer1ep6YlRsbKzbMUpqSo4BAJeL4OBgj9YBgC+r1iF5ypQp2rhxo/bv36/Nmzfrj3/8owIDAzVo0CDZbDYlJydr0qRJ+vTTT7V9+3YNGzZMsbGx6tq1qySpZ8+eatOmje655x598cUX+vDDDzV9+nSNHTtWISEhkqTRo0dr3759euihh7Rnzx4tWLBAb7/9tiZOnOjNUweAKlerVi2P1gGAL6vWa5IPHTqkQYMG6ccff1TDhg110003acuWLWrYsKEkae7cuQoICNAdd9yhs2fPKiEhQQsWLHB9PjAwUKtXr9aYMWMUGxur2rVra+jQoXryySddNU2bNtWaNWs0ceJEzZs3T40bN9Yrr7zC9m8ALjutW7e+5M4+JXUA4O8shmEY3m7CHzgcDtlsNuXn57M+GYBPys3NdXsg04W8++67Cg8Pr4KOAMCzKpLXqvVyCwBA1fnggw88WgcAvoyQDACQREgGgPMRkgEAkqQffvjBo3UA4MsIyQAASVJ5b1HhVhYAlwNCMgAAAGBCSAYAAABMCMkAAACACSEZAAAAMCEkAwAkSRaLxaN1AODLCMkAAEkq99NCeaoogMsBIRkAIEkqLi72aB0A+DJCMgBAkvTTTz95tA4AfBkhGQAAADAhJAMAAAAmhGQAgCQpMDDQo3UA4MsIyQAASdIVV1zh0ToA8GWEZACAJCkvL8+jdQDgywjJAABJksPh8GgdAPgyQjIAAABgQkgGAAAATAjJAAAAgAkhGQAAADAhJAMAJEk2m82jdQDgywjJAABJ0o033ujROgDwZYRkAIAk6fjx4x6tAwBfRkgGAEiS8vPzPVoHAL6MkAwAkCT9+OOPHq0DAF9GSAYASJJOnz7t0ToA8GWEZACAJOncuXMerQMAX0ZIBgBIkgzD8GgdAPgyQjIAAABgQkgGAAAATAjJAAAAgAkhGQAAADAhJAMAAAAmhGQAgCQpLCzMo3UA4MsIyQAASZLFYvFoHQD4MkIyAECSdObMGY/WAYAvIyQDACQRkgHgfIRkAIAkqbi42KN1AODLCMkAAACACSEZAAAAMCEkAwAAACaEZAAAAMCEkAwAAACYEJIBAAAAE0IyAAAAYEJIBgAAAEwIyQAAAIAJIRkAAAAwISQDAAAAJoRkAAAAwISQDAAAAJgQkgEAAAATQjIAAABgQkgGAAAATAjJAAAAgAkhGQAAADAhJAMAAAAmhGQAAADAhJAMAAAAmBCSAQAAABNCMgAAAGBCSAYAAABMCMkAAACACSEZAAAAMCEkAwAAACaEZAAAAMCkWofk2bNn64YbblDdunXVqFEj9e/fX3v37nWr6datmywWi9tr9OjRbjUHDhxQ3759VatWLTVq1EhTp05VUVGRW82GDRt0/fXXKyQkRFdffbWWLFlS2acHAACAaqpah+SNGzdq7Nix2rJli9atW6dz586pZ8+eKigocKsbOXKkDh8+7Ho9++yzrjmn06m+ffuqsLBQmzdv1j//+U8tWbJEjz/+uKsmOztbffv21a233qrMzExNmDBBI0aM0Icfflhl5woAAIDqw2IYhuHtJsrr2LFjatSokTZu3Kj4+HhJP19J7tChg1544YUyP/PBBx+oX79+ysnJUUREhCQpJSVFDz/8sI4dO6bg4GA9/PDDWrNmjXbt2uX63MCBA5WXl6e1a9eWedyzZ8/q7NmzrvcOh0MxMTHKz8+X1Wr10BkDQNXp1q1buWs3bNhQaX0AQGVxOByy2WzlymvV+kqyWX5+viQpPDzcbXzp0qVq0KCB2rZtq2nTpunUqVOuuYyMDLVr184VkCUpISFBDodDu3fvdtX06NHD7ZgJCQnKyMi4YC+zZ8+WzWZzvWJiYn7z+QEAAKB6CPJ2A+VVXFysCRMmKC4uTm3btnWNDx48WFdeeaWio6OVlZWlhx9+WHv37tW7774rSbLb7W4BWZLrvd1uv2iNw+HQ6dOnVbNmzVL9TJs2TZMmTXK9L7mSDAAAAN/nMyF57Nix2rVrl/773/+6jY8aNcr1c7t27RQVFaXu3bvru+++U/PmzSutn5CQEIWEhFTa8QEAAOA9PrHcYty4cVq9erU+/fRTNW7c+KK1Xbp0kSR9++23kqTIyEgdOXLErabkfWRk5EVrrFZrmVeRAQAA4N+qdUg2DEPjxo3Te++9p08++URNmza95GcyMzMlSVFRUZKk2NhYffnllzp69KirZt26dbJarWrTpo2rZv369W7HWbdunWJjYz10JgAAAPAl1Tokjx07Vq+//rreeOMN1a1bV3a7XXa7XadPn5Ykfffdd/rLX/6i7du3a//+/Vq5cqWGDBmi+Ph4tW/fXpLUs2dPtWnTRvfcc4+++OILffjhh5o+fbrGjh3rWi4xevRo7du3Tw899JD27NmjBQsW6O2339bEiRO9du4AAADwnmq9BZzFYilz/LXXXtO9996rgwcP6u6779auXbtUUFCgmJgY/fGPf9T06dPdtvX4/vvvNWbMGG3YsEG1a9fW0KFD9fTTTyso6Jcl2Rs2bNDEiRP11VdfqXHjxnrsscd07733lrvXimwpAgDVEVvAAfB3Fclr1Tok+xJCMgBfR0gG4O/8dp9kAAAAoCoQkgEAAAATQjIAAABgQkgGAAAATAjJAAAAgAkhGQAAADAhJAMAAAAmhGQAAADAhJAMAAAAmARdugQAfnbmzBkdOHDA222gGvjmm2+83QIqSZMmTRQaGurtNgCvIyQDKLcDBw5o1KhR3m4D1QD/PfBfixYtUosWLbzdBuB1hGQA5dakSRMtWrTI222gklQk+PLfA//VpEkTb7cAVAuEZADlFhoayhUmP/bKK69oxIgR5aq7+uqrq6AjAPAebtwDAEhSuYMvARnA5YCQDABw2bBhw2+aBwB/QUgGALjZsGGDXnnlFVksFkmSxWLRK6+8QkAGcFkhJAMASrn66qv18ssvS5JefvllllgAuOwQkgEAAAATQjIAAABgQkgGAAAATAjJAAAAgAkhGQAAADAhJAMAAAAmhGQAAADAhJAMAAAAmBCSAQAAABNCMgAAAGBCSAYAAABMCMkAAACACSEZAAAAMCEkAwAAACaEZAAAAMCEkAwAAACYEJIBAAAAE0IyAAAAYEJIBgAAAEwIyQAAAIAJIRkAAAAwISQDAAAAJoRkAAAAwISQDAAAAJgQkgEAAAATQjIAAABgQkgGAAAATAjJAAAAgEmQtxuA7zty5Ijy8/O93QYAD/v+++/d/hOAf7HZbIqIiPB2G9WWxTAMw9tN+AOHwyGbzab8/HxZrVZvt1Nljhw5orvvGaJzhWe93QoAAKiAGsEhev3f/7qsgnJF8hpXkvGb5Ofn61zhWZ1udouKQ23ebgcAAJRDwJl8ad9G5efnX1YhuSIIyfCI4lCbims38HYbAAAAHsGNewAAAIAJIRkAAAAwISQDAAAAJoRkAAAAwISQDAAAAJgQkgEAAAATQjIAAABgQkgGAAAATAjJAAAAgAkhGQAAADAhJAMAAAAmhGQAAADAhJAMAAAAmBCSAQAAABNCMgAAAGBCSAYAAABMgrzdAPxDwOk8b7cAAADKib+3L42QDI+omZ3u7RYAAAA8hpBsMn/+fD333HOy2+267rrr9NJLL+nGG2/0dlvV3umm8SquWc/bbQAAgHIIOJ3HBa5LICSf56233tKkSZOUkpKiLl266IUXXlBCQoL27t2rRo0aebu9aq24Zj0V127g7TYAAAA8gpB8nueff14jR47UsGHDJEkpKSlas2aNXn31VT3yyCNe7q56CziT7+0WAABAOfH39qURkv+/wsJCbd++XdOmTXONBQQEqEePHsrIyChVf/bsWZ09e9b13uFwVEmf1Y3NZlON4BBp30ZvtwIAACqgRnCIbDabt9uotgjJ/9/x48fldDoVERHhNh4REaE9e/aUqp89e7ZmzpxZVe1VWxEREXr93/9Sfj7/j/Ry8P3332vWrFnebgNAJXr00Ud15ZVXersNVAGbzVYq9+AXhORfadq0aZo0aZLrvcPhUExMjBc78p6IiAj+R3aZaNKkiRYtWuTtNgBUoiZNmig0NNTbbQBeR0j+/xo0aKDAwEAdOXLEbfzIkSOKjIwsVR8SEqKQkJCqag+oFkJDQ9WiRQtvtwEAQKXjiXv/X3BwsDp16qT169e7xoqLi7V+/XrFxsZ6sTMAAABUNa4kn2fSpEkaOnSoOnfurBtvvFEvvPCCCgoKXLtdAAAA4PJASD7PXXfdpWPHjunxxx+X3W5Xhw4dtHbtWtbbAgAAXGYshmEY3m7CHzgcDtlsNuXn58tqtXq7HQAAAJhUJK+xJhkAAAAwISQDAAAAJoRkAAAAwISQDAAAAJgQkgEAAAATQjIAAABgQkgGAAAATAjJAAAAgAkhGQAAADAhJAMAAAAmhGQAAADAhJAMAAAAmBCSAQAAABNCMgAAAGBCSAYAAABMCMkAAACACSEZAAAAMCEkAwAAACaEZAAAAMCEkAwAAACYBHm7AX9hGIYkyeFweLkTAAAAlKUkp5XktoshJHvITz/9JEmKiYnxcicAAAC4mJ9++kk2m+2iNRajPFEal1RcXKycnBzVrVtXFovF2+0AwG/mcDgUExOjgwcPymq1ersdAPjNDMPQTz/9pOjoaAUEXHzVMSEZAFAmh8Mhm82m/Px8QjKAyw437gEAAAAmhGQAAADAhJAMAChTSEiInnjiCYWEhHi7FQCocqxJBgAAAEy4kgwAAACYEJIBAAAAE0IyAAAAYEJIBgAAAEwIyQAAAIAJIRkAAAAwISQDAAAAJoRkAAAAwOT/AX3m83YopCR2AAAAAElFTkSuQmCC
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<p>For the box plot we defines a function called finding_outliers that can be used to identify outliers in a dataset.</p>
<ul>
<li>First we calculate the interquartile range (IQR) of the variable by subtracting the value at the 25th percentile (Q1) from the value at the 75th percentile (Q3) and then define two thresholds for outliers. These thresholds are based on the IQR.</li>
<li>By creating a boxplot of the total jobs in block we can visualize the distribution of the data and identify outliers and boxplots shows the quartiles (Q1, Q3) and the median (Q2) of the data. The whiskers extend from the box to data points within 1.5 IQRs of the quartiles. Data points further than 1.5 IQRs from the quartiles are considered outliers and plotted as individual points.</li>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [31]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Boxplot with rotated x-labels</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">,</span> <span class="s2">"Boxplot"</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">transAxes</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">"Region"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"Total jobs in block"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">melbourne_job</span><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s2">"Set3"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span> <span class="c1"># Adjust the rotation angle as needed</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<pre><ipython-input-31-c3b914a8343c>:4: FutureWarning:
Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.
sns.boxplot(x="Region", y="Total jobs in block", data=melbourne_job, palette="Set3")
</pre>
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<p>The boxplot you provided visualizes the distribution of "Total jobs in block" across different regions. By looking at the plot, you can see how the median and IQR vary across regions. You can also identify any outliers for each region.The x axis show the distribution of "Total jobs in block" for each region and y axis set to "Total jobs in block" and the plot shows the interquartile range (IQR) of the data, which is the range between the first quartile (Q1) and the third quartile (Q3). The line in the middle of the box represents the median (Q2) of the data. The whiskers extend from the box to data points within 1.5 IQRs of the quartiles. Data points further than 1.5 IQRs from the quartiles are considered outliers and plotted as individual points.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [32]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">melbourne_job</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>The histogram of the variable shows the "Total jobs in block" in the "melbourne_job" dataset where the x-axis shows the number of jobs in a block, while the y-axis shows the number of listings in the dataset that have that number of jobs and according to the plot, there seem to be more job listings with a value in the range of 2000 to 2500 total jobs in block compared to listings with a value in the range of 1000 to 1500 total jobs in block. and The histogram appears to be bell-shaped, which shows that the data is normally distributed. A normal distribution is a symmetrical distribution that resembles a bell curve</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [33]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="n">ax</span><span class="o">=</span><span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">"Region"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">melbourne_job</span><span class="p">,</span><span class="n">palette</span><span class="o">=</span><span class="s2">"Set3"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">get_xticklabels</span><span class="p">(),</span><span class="n">rotation</span> <span class="o">=</span> <span class="mi">45</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Count of Jobs by Region"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<pre><ipython-input-33-3a639717183a>:2: FutureWarning:
Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.
ax=sns.countplot(x="Region", data=melbourne_job,palette="Set3")
<ipython-input-33-3a639717183a>:3: UserWarning: FixedFormatter should only be used together with FixedLocator
ax.set_xticklabels(ax.get_xticklabels(),rotation = 45)
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<p>A bar chart titled count of regions in melbourne job data. The x-axis shows the regions in Melbourne. The y-axis shows the number of jobs. The most common region is Melbourne (CBD) with over 1400 jobs. Other regions include West Melbourne (Residential), Carlton, North Melbourne, South Yarra, and Parkville</p>
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<p>(EDA) - Exploratory Data Analysis for House Prices by Small Area - Sale Year</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [34]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">houseprice_melbourne</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre><class 'pandas.core.frame.DataFrame'>
Index: 316 entries, 0 to 346
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Year 316 non-null int64
1 Region 316 non-null object
2 House_type 316 non-null object
3 Median_Price 316 non-null float64
4 Transaction_Count 316 non-null int64
dtypes: float64(1), int64(2), object(2)
memory usage: 14.8+ KB
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<div class="jp-InputPrompt jp-InputArea-prompt">In [35]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Plotting the distribution of prices</span>
<span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">df_filtered4</span><span class="p">[</span><span class="s2">"Median_Price"</span><span class="p">],</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"blue"</span> <span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">" distribution of prices"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># Creating a probability plot</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">prob</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">df_filtered4</span><span class="p">[</span><span class="s2">"Median_Price"</span><span class="p">],</span> <span class="n">plot</span><span class="o">=</span><span class="n">plt</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>For the above analysis performed visualization of the distribution of a dataset called "Median_Price" along with a probability plot.</p>
<ul>
<li>The distribution plot, created with the function sns.histplot, shows the probability density of the dataThe x-axis represents the median price, while the y-axis represents the density. The curve depicts a distribution, and the line superimposed on the curve is the probability density function (PDF) of a normal distribution that has been fitted to the data.</li>
<li>The probability plot, created with the function stats.probplot, shows how well the data follows a normal distribution. The x-axis represents the ordered values from the data set, while the y-axis represents the expected cumulative probability values for a normal distribution and the points deviate slightly from a straight line, which suggests that the data may not be perfectly normal. However, the deviation is minor, so it is likely that the normal distribution is a reasonable approximation for the data.</li>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [36]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">,</span> <span class="s2">"Boxplot"</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">transAxes</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">"Median_Price"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">houseprice_melbourne</span><span class="p">)</span>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[36]:</div>
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<pre><Axes: ylabel='Median_Price'></pre>
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
"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [37]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">finding_outliers</span><span class="p">(</span><span class="n">houseprice_melbourne</span><span class="p">,</span> <span class="s2">"Median_Price"</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">"Median_Price"</span><span class="p">)</span>
<span class="c1"># For price</span>
<span class="n">iqr_price</span> <span class="o">=</span> <span class="n">houseprice_melbourne</span><span class="p">[</span><span class="s2">"Median_Price"</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span> <span class="o">-</span> <span class="n">houseprice_melbourne</span><span class="p">[</span><span class="s2">"Median_Price"</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span>
<span class="n">houseprice_melbourne</span><span class="p">[</span><span class="s2">"Median_Price"</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.5</span> <span class="o">*</span> <span class="n">iqr_price</span>
<span class="n">houseprice_melbourne</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">finding_outliers</span><span class="p">(</span><span class="n">houseprice_melbourne</span><span class="p">,</span> <span class="s2">"Median_Price"</span><span class="p">)</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="s2">"Median_Price"</span><span class="p">)]</span> <span class="o">=</span> <span class="n">houseprice_melbourne</span><span class="p">[</span><span class="s2">"Median_Price"</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.5</span> <span class="o">*</span> <span class="n">iqr_price</span>
<span class="c1"># Price boxplot</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">,</span> <span class="s2">"Boxplot of Median House Prices"</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">transAxes</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">"Median_Price"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">houseprice_melbourne</span><span class="p">)</span>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[37]:</div>
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<pre><Axes: ylabel='Median_Price'></pre>
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<p>For the box plot it visualize and treat outliers in a dataset named "houseprice_melbourne" which contains information about house prices in Melbourne.The outlier values in the "Median_Price" column is replaced with the upper threshold for outliers, effectively treating the outliers by setting them to the maximum value within the IQR range and in the above plot it finds outliers in the median house price data, treats the outliers using winsorization, and visualizes the distribution of the data before and after treating outliers using boxplots</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [38]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">"Median_Price"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"Region"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">houseprice_melbourne</span><span class="p">,</span><span class="n">palette</span><span class="o">=</span><span class="s2">"Set3"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Boxplot of Median House Prices by Region"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre><ipython-input-38-80c41d38d753>:2: FutureWarning:
Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.
sns.boxplot(x="Median_Price", y="Region", data=houseprice_melbourne,palette="Set3")
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<p>The box plot created with sns.boxplot and shows the median house price of houses in Melbourne by region.
x-axis represents the different regions in Melbourne and y-axis represents the median house price. The scale goes from 0 to around 1.2 million.</p>
<ul>
<li>Docklands appears to have the highest median house price, followed by South Yarra and Melbourne (CBD)</li>
<li>Some regions, like West Melbourne (Residential) and Carlton, have a wider range of house prices as indicated by the larger size of their boxes. This suggests a higher variability in house prices within these regions.</li>
<li>There are a few outliers, which could be due to particularly expensive or inexpensive houses in those regions.</li>
<li>This plot helps to identify regions with generally more expensive or affordable houses, along with the range of prices within each region.</li>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [39]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">houseprice_melbourne</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>The histogram of the variable shows the "Median_Price" variable in the "houseprice_melbourne" dataset.The x-axis shows the median house price, while the y-axis shows the number of houses in the dataset that have that median price,In the plot, there seems to be a concentration of houses in the price range of 500,000 to 700,000, with fewer houses outside that range.
The histogram appears to be right-skewed, which means that there are more houses towards the lower end of the price range and the tail of the distribution extends towards the higher end. This suggests that most houses in the dataset are priced below a certain threshold, but there are also some very expensive houses.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [40]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1">#plt.figure(figsize=(15,8))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">"House_type"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"Median_Price"</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s2">"Region"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">houseprice_melbourne</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Boxplot of Median House Prices by House Type and Region"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>The aboce plot appears to be a visualization of the median house price for different house types across various regions in Melbourne. It was created with sns.boxplot from seaborn.</p>
<ul>
<li>x-axis represents the house type - House/Townhouse or Residential Apartment and y-axis Y-axis represents the median house price. The scale goes from 0 to around 1.2 million and Each box represents the distribution of the median house price for a particular house type within a region.</li>
<li>From the plot houses/townhouses tend to have a higher median price compared to residential apartments across all regions.</li>
<li>There is a significant variation in the median house price across different regions, regardless of house type.Docklands appears to have the highest median house price for both house types, followed by South Yarra and Melbourne (CBD).</li>
<li>Region Carlton appears to have a wider range of house prices for houses/townhouses compared to Docklands.</li>
<li>This box plot helps to identify trends and potential outliers in the data.</li>
</ul>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [41]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">num_bins</span> <span class="o">=</span> <span class="mi">10</span>
<span class="c1"># Define the bin edges using numpy's linspace function</span>
<span class="n">bin_edges</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">houseprice_melbourne</span><span class="p">[</span><span class="s1">'Transaction_Count'</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">houseprice_melbourne</span><span class="p">[</span><span class="s1">'Transaction_Count'</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">num_bins</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># Create the count plot with specified bins</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">houseprice_melbourne</span><span class="p">[</span><span class="s1">'Transaction_Count'</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><span class="n">bin_edges</span><span class="p">),</span> <span class="n">data</span><span class="o">=</span><span class="n">houseprice_melbourne</span><span class="p">)</span>
<span class="c1"># Rotate x-axis labels for better visibility if necessary</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Count of Transactions by Bins of Transaction Count"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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XWNGTMmioqKVrqm5s2bx9577527XFBQEAMGDIjXX389pk2bFhER+++/f1SvXj1uu+22XLvHHnssvvnmm59dN2zJxzFu3LgYN25c3HPPPTFo0KD4+9//vtSYWZ7DDz+8zPprP+730hlpjz32WMybN2+F7jPih8NoZ8yYESeccEKZ9fB222232HjjjX/VdRSrVasWhx9++FLbV+S5Wap27dpl+qBq1aqx1VZb5f4uET88R7744oulDple3u8sKiqKmTNnRrt27aJevXplxuE999wTm222WZnnTKksY2Jl90d9+vQpM1OwU6dOUVBQUObxLkthYeEK789/zefEz+2/AECwBsBaoaCgICJ++FC7Ij777LPIz8+Pdu3aldnetGnTqFevXuYQLCJivfXWK3O59EPZT4VGP+Wzzz6L5s2bL/Uhs/Qwz19S689Zf/31l7l91KhR0alTp6hevXo0aNAgGjVqFA8//PAy18b6ub9HtWrV4s9//nM8+uij0aRJk9h+++3jsssuy4VEpSZNmhR777131K1bNwoKCqJRo0a5gKL093788ccR8cNZV1eFr7/+OubNmxcbbbTRUtdtsskmUVJSEp9//nmZ7T/3eHv27Bn9+/ePCy+8MBo2bBh77bVXjBw5cqm1/ZanXbt2SwUiG264YUREbn2tevXqxR577FFmTa7bbrst1l133ejdu/cK/Z4NNtgg+vTpE3369Il99tkn/va3v8UJJ5wQV199dbz99ts/e/uf+zusv/76MXjw4LjpppuiYcOG0bdv37juuut+dn210uf7svpk4403/lXHw7rrrrvMkzWsyHOzVIsWLZbqv3XWWafM/uHss8+O2rVrx1ZbbRUbbLBBDBo0KJ5//vkyt5k/f36cd955uXXOGjZsGI0aNYpZs2aV+Z0ff/zxKhsPESu/P/rx8yBi6ce7LAUFBSu1P4/4dZ4Tq3p/DsDaR7AGwFqhoKAgmjdvHu+8885K3e6XLDK9rEXzI36Y0bUs6UcLe68JlpwVU+pf//pXHHbYYdG2bdv4xz/+EWPHjo1x48ZF7969o6SkZKn2K/L3OPXUU+PDDz+MYcOGRfXq1eOPf/xjbLLJJvH6669HxA+Lr/fs2TPefPPNuOiii+Khhx6KcePG5c4KuKzfW15+7vHm5eXF3XffHS+++GKceOKJ8d///jeOOOKI6Nq1a8ydO3eV1TFgwID45JNP4oUXXog5c+bEgw8+GAcddFDk52d/+7fjjjtGRMSzzz77s21XpN+vvPLKeOutt+Kcc86J+fPnx8knnxzt27ePL774InONv6ZljYeVfW6uyN9lk002iQ8++CBuv/326NGjR9xzzz3Ro0ePOP/883NtTjrppBg6dGjsv//+ceedd8bjjz8e48aNiwYNGqxR42F5Nt5445g9e/ZSwfUvtbx9/m9hfw7Ar0OwBsBaY/fdd4+PP/44XnzxxZ9t26pVqygpKYmPPvqozPbp06fHrFmzolWrVrlt66yzTsyaNatMu0WLFsVXX32VudaVCfRatWoVX3755VKzN0oPMVuy1tXh7rvvjjZt2sS9994bhx56aPTt2zf69OkTCxYs+EX327Zt2zj99NPj8ccfj3feeScWLVoUV155ZUT8cKbJmTNnxi233BKnnHJK7L777tGnT58yh2iV3kdE/GzAuqJ//0aNGkXNmjXjgw8+WOq6999/P/Lz86Nly5YrdF8/tvXWW8fQoUPj1Vdfjdtuuy0mTZoUt99++8/ebvLkyUt9qP/www8jIsqc1XDnnXeORo0axW233Rb33XdfzJs3b4UXgl+exYsXR0Ss0gCwY8eOce6558azzz4b//nPf+K///1vDB8+fLntS5/vy+qTDz74YLWPhxV9bq6sWrVqxQEHHBAjR46MqVOnxm677RZDhw7NjbO77747Bg4cGFdeeWXsu+++8bvf/S569Oix1L6qbdu2q2w8RKy+/dEee+wRET8E+StSU8SKPSeWtT+P+GUzf50FFOC3TbAGwFrjrLPOilq1asVRRx0V06dPX+r6jz/+OK655pqIiNh1110j4oczdC7pL3/5S0T8sDZPqbZt2y41Q2fEiBHLneGwImrVqrXMD3fLsuuuu0ZxcXH87W9/K7P9qquuiry8vNyZBFeX0hkcS4Y7EyZMWKFAc1nmzZu3VCjXtm3bqFOnTu7wyGX9zkWLFsX1119f5nabb755rL/++nH11Vcv9fdd8ra1atWKiPjZPqhUqVLstNNO8cADD+QOs4z4IYAdPXp09OjRI3cY8or67rvvlgrGSs+kuiKHg3755ZdlzlpbWFgYt956a3Tu3DmaNm2a2165cuU46KCD4s4774xbbrklOnbsGJ06dVqpWn/soYceioiIzTbb7BfdT8QPdZcGdaU6duwY+fn5P/l32GKLLaJx48YxfPjwMu0effTReO+998qM3dVhRZ+bK2PmzJllLletWjU23XTTSCnl1uWrVKnSUs+ja6+9dqn9Uv/+/ePNN99c5pmOS2+/ouMhYvXtj/bdd9/o2LFjDB06dJn7ljlz5uTOqrwyz4m2bdvG+++/H19//XVu25tvvrnUobYrY2X+fgCsfSqXdwEAsKq0bds2Ro8eHQcccEBssskmMWDAgOjQoUMsWrQoXnjhhbjrrrvisMMOi4gfgoGBAwfGiBEjcodyvfzyyzFq1Kjo169f7LDDDrn7Peqoo+K4446L/v37x+9+97t4880347HHHouGDRtmrrVr165xww03xCWXXBLt2rWLxo0bL3ftqz322CN22GGH+MMf/hCffvppbLbZZvH444/HAw88EKeeemqZhcFXh9133z3uvffe2HvvvWO33XaLKVOmxPDhw2PTTTfNNJPpww8/jB133DH233//2HTTTaNy5cpx3333xfTp0+PAAw+MiIhtttkm1llnnRg4cGCcfPLJkZeXF//85z+XChby8/PjhhtuiD322CM6d+4chx9+eDRr1izef//9mDRpUjz22GMR8cPfPyLi5JNPjr59+0alSpVyv+vHLrnkkhg3blz06NEjTjjhhKhcuXL8/e9/j4ULF8Zll1220o931KhRcf3118fee+8dbdu2jTlz5sSNN94YBQUFucD3p2y44YZx5JFHxiuvvBJNmjSJm2++OaZPnx4jR45cqu2AAQPir3/9azz99NO5QxNX1GuvvZabLTRnzpx48skn45577oltttkmdtppp5W6r2V56qmn4sQTT4z99tsvNtxww1i8eHH885//jEqVKkX//v2Xe7sqVarEn//85zj88MOjZ8+ecdBBB8X06dPjmmuuidatW8dpp532i2tbGSv63FwZO+20UzRt2jS23XbbaNKkSbz33nvxt7/9LXbbbbfc2ma77757/POf/4y6devGpptuGi+++GI88cQT0aBBgzL3deaZZ8bdd98d++23X+6Q42+//TYefPDBGD58eGy22WbRtm3bqFevXgwfPjzq1KkTtWrVim7dui1zjcXVtT+qUqVK3HvvvdGnT5/YfvvtY//9949tt902qlSpEpMmTYrRo0fHOuusE0OHDl2p58QRRxwRf/nLX6Jv375x5JFHxowZM2L48OHRvn37pU5EsqJWZn8CwFpo9Z6EFAB+fR9++GE6+uijU+vWrVPVqlVTnTp10rbbbpuuvfbatGDBgly7oqKidOGFF6b1118/ValSJbVs2TINGTKkTJuUUiouLk5nn312atiwYapZs2bq27dvmjx5cmrVqlUaOHBgrt3IkSNTRKRXXnmlzO2ffvrpFBHp6aefzm2bNm1a2m233VKdOnVSRKSePXv+5GOaM2dOOu2001Lz5s1TlSpV0gYbbJAuv/zyVFJSUqZdz549U/v27VfuD5ZSqlWrVpnHMmXKlBQR6fLLL1+qbUlJSbr00ktTq1atUrVq1VKXLl3SmDFj0sCBA1OrVq1W6D4iIp1//vkppZS++eabNGjQoLTxxhunWrVqpbp166Zu3bqlO++8s8xtnn/++bT11lunGjVqpObNm6ezzjorPfbYY0v9bVNK6bnnnku/+93vUp06dVKtWrVSp06d0rXXXpu7fvHixemkk05KjRo1Snl5eWnJt0RL1lbqtddeS3379k21a9dONWvWTDvssEN64YUXyrRZ0f5/7bXX0kEHHZTWW2+9VK1atdS4ceO0++67p1dffXWpv9OPtWrVKu22227pscceS506dUrVqlVLG2+8cbrrrruWe5v27dun/Pz89MUXX/zs/af0v35b8qdy5cqpTZs26cwzz0xz5swp075nz55lnr+lj/fHNZXe78iRI1NKKX3yySfpiCOOSG3btk3Vq1dP9evXTzvssEN64oknVqjOO+64I3Xp0iVVq1Yt1a9fPx188MFLPcbl9cnPGTRoUPrx2+SfGlsr+txc3n38eOz8/e9/T9tvv31q0KBBqlatWmrbtm0688wz0+zZs3Ntvvvuu3T44Yenhg0bptq1a6e+ffum999/f6n9UkopzZw5M5144olp3XXXTVWrVk0tWrRIAwcOTN98802uzQMPPJA23XTTVLly5TL99OPaUlrx/VFEpEGDBi31eJdV4/J899136bzzzksdO3ZMNWvWTNWrV08dOnRIQ4YMSV999VWZtivynEgppX/961+pTZs2qWrVqqlz587psccey7z/Sumn9ycArP3yUrLyJgDA2qpLly5Rv379ePLJJ8u7FACAtY411gAA1lKvvvpqvPHGGzFgwIDyLgUAYK1kxhoAwFrmnXfeiYkTJ8aVV14Z33zzTXzyySdRvXr18i4LAGCtY8YaAMBa5u67747DDz88ioqK4t///rdQDQDgV2LGGgAAAABkYMYaAAAAAGQgWAMAAACADCqXdwEVQUlJSXz55ZdRp06dyMvLK+9yAAAAACgnKaWYM2dONG/ePPLzf3pOmmAtIr788sto2bJleZcBAAAAQAXx+eefR4sWLX6yjWAtIurUqRMRP/zBCgoKyrkaAAAAAMpLYWFhtGzZMpcX/RTBWkTu8M+CggLBGgAAAAArtFyYkxcAAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIoHJ5F7Am6nrmreVdwm/OxMsHlHcJAAAAAGWYsQYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyKBcg7Vhw4bFlltuGXXq1InGjRtHv3794oMPPijTplevXpGXl1fm57jjjivTZurUqbHbbrtFzZo1o3HjxnHmmWfG4sWLV+dDAQAAAOA3pnJ5/vLx48fHoEGDYsstt4zFixfHOeecEzvttFO8++67UatWrVy7o48+Oi666KLc5Zo1a+b+X1xcHLvttls0bdo0Xnjhhfjqq69iwIABUaVKlbj00ktX6+MBAAAA4LejXIO1sWPHlrl8yy23ROPGjWPixImx/fbb57bXrFkzmjZtusz7ePzxx+Pdd9+NJ554Ipo0aRKdO3eOiy++OM4+++y44IILomrVqr/qYwAAAADgt6lCrbE2e/bsiIioX79+me233XZbNGzYMDp06BBDhgyJefPm5a578cUXo2PHjtGkSZPctr59+0ZhYWFMmjRpmb9n4cKFUVhYWOYHAAAAAFZGuc5YW1JJSUmceuqpse2220aHDh1y2//v//4vWrVqFc2bN4+33norzj777Pjggw/i3nvvjYiIadOmlQnVIiJ3edq0acv8XcOGDYsLL7zwV3okAAAAAPwWVJhgbdCgQfHOO+/Ec889V2b7Mccck/t/x44do1mzZrHjjjvGxx9/HG3bts30u4YMGRKDBw/OXS4sLIyWLVtmKxwAAACA36QKcSjoiSeeGGPGjImnn346WrRo8ZNtu3XrFhERkydPjoiIpk2bxvTp08u0Kb28vHXZqlWrFgUFBWV+AAAAAGBllGuwllKKE088Me6777546qmnYv311//Z27zxxhsREdGsWbOIiOjevXu8/fbbMWPGjFybcePGRUFBQWy66aa/St0AAAAAUK6Hgg4aNChGjx4dDzzwQNSpUye3JlrdunWjRo0a8fHHH8fo0aNj1113jQYNGsRbb70Vp512Wmy//fbRqVOniIjYaaedYtNNN41DDz00Lrvsspg2bVqce+65MWjQoKhWrVp5PjwAAAAA1mLlOmPthhtuiNmzZ0evXr2iWbNmuZ877rgjIiKqVq0aTzzxROy0006x8cYbx+mnnx79+/ePhx56KHcflSpVijFjxkSlSpWie/fuccghh8SAAQPioosuKq+HBQAAAMBvQLnOWEsp/eT1LVu2jPHjx//s/bRq1SoeeeSRVVUWAAAAAPysCnHyAgAAAABY0wjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyKNdgbdiwYbHllltGnTp1onHjxtGvX7/44IMPyrRZsGBBDBo0KBo0aBC1a9eO/v37x/Tp08u0mTp1auy2225Rs2bNaNy4cZx55pmxePHi1flQAAAAAPiNKddgbfz48TFo0KB46aWXYty4cVFUVBQ77bRTfP/997k2p512Wjz00ENx1113xfjx4+PLL7+MffbZJ3d9cXFx7LbbbrFo0aJ44YUXYtSoUXHLLbfEeeedVx4PCQAAAIDfiLyUUirvIkp9/fXX0bhx4xg/fnxsv/32MXv27GjUqFGMHj069t1334iIeP/992OTTTaJF198Mbbeeut49NFHY/fdd48vv/wymjRpEhERw4cPj7PPPju+/vrrqFq16s/+3sLCwqhbt27Mnj07CgoKfrZ91zNv/WUPlJU28fIB5V0CAAAA8BuwMjlRhVpjbfbs2RERUb9+/YiImDhxYhQVFUWfPn1ybTbeeONYb7314sUXX4yIiBdffDE6duyYC9UiIvr27RuFhYUxadKkZf6ehQsXRmFhYZkfAAAAAFgZFSZYKykpiVNPPTW23Xbb6NChQ0RETJs2LapWrRr16tUr07ZJkyYxbdq0XJslQ7XS60uvW5Zhw4ZF3bp1cz8tW7ZcxY8GAAAAgLVdhQnWBg0aFO+8807cfvvtv/rvGjJkSMyePTv38/nnn//qvxMAAACAtUvl8i4gIuLEE0+MMWPGxLPPPhstWrTIbW/atGksWrQoZs2aVWbW2vTp06Np06a5Ni+//HKZ+ys9a2hpmx+rVq1aVKtWbRU/CgAAAAB+S8p1xlpKKU488cS477774qmnnor111+/zPVdu3aNKlWqxJNPPpnb9sEHH8TUqVOje/fuERHRvXv3ePvtt2PGjBm5NuPGjYuCgoLYdNNNV88DAQAAAOA3p1xnrA0aNChGjx4dDzzwQNSpUye3JlrdunWjRo0aUbdu3TjyyCNj8ODBUb9+/SgoKIiTTjopunfvHltvvXVEROy0006x6aabxqGHHhqXXXZZTJs2Lc4999wYNGiQWWkAAAAA/GrKNVi74YYbIiKiV69eZbaPHDkyDjvssIiIuOqqqyI/Pz/69+8fCxcujL59+8b111+fa1upUqUYM2ZMHH/88dG9e/eoVatWDBw4MC666KLV9TAAAAAA+A3KSyml8i6ivBUWFkbdunVj9uzZUVBQ8LPtu55562qoiiVNvHxAeZcAAAAA/AasTE5UYc4KCgAAAABrEsEaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAG5RqsPfvss7HHHntE8+bNIy8vL+6///4y1x922GGRl5dX5mfnnXcu0+bbb7+Ngw8+OAoKCqJevXpx5JFHxty5c1fjowAAAADgt6hcg7Xvv/8+Nttss7juuuuW22bnnXeOr776Kvfz73//u8z1Bx98cEyaNCnGjRsXY8aMiWeffTaOOeaYX7t0AAAAAH7jKpfnL99ll11il112+ck21apVi6ZNmy7zuvfeey/Gjh0br7zySmyxxRYREXHttdfGrrvuGldccUU0b958ldcMAAAAABFrwBprzzzzTDRu3Dg22mijOP7442PmzJm561588cWoV69eLlSLiOjTp0/k5+fHhAkTlnufCxcujMLCwjI/AAAAALAyKnSwtvPOO8ett94aTz75ZPz5z3+O8ePHxy677BLFxcURETFt2rRo3LhxmdtUrlw56tevH9OmTVvu/Q4bNizq1q2b+2nZsuWv+jgAAAAAWPuU66GgP+fAAw/M/b9jx47RqVOnaNu2bTzzzDOx4447Zr7fIUOGxODBg3OXCwsLhWsAAAAArJQKPWPtx9q0aRMNGzaMyZMnR0RE06ZNY8aMGWXaLF68OL799tvlrssW8cO6bQUFBWV+AAAAAGBlrFHB2hdffBEzZ86MZs2aRURE9+7dY9asWTFx4sRcm6eeeipKSkqiW7du5VUmAAAAAL8B5Xoo6Ny5c3OzzyIipkyZEm+88UbUr18/6tevHxdeeGH0798/mjZtGh9//HGcddZZ0a5du+jbt29ERGyyySax8847x9FHHx3Dhw+PoqKiOPHEE+PAAw90RlAAAAAAflXlOmPt1VdfjS5dukSXLl0iImLw4MHRpUuXOO+886JSpUrx1ltvxZ577hkbbrhhHHnkkdG1a9f4z3/+E9WqVcvdx2233RYbb7xx7LjjjrHrrrtGjx49YsSIEeX1kAAAAAD4jSjXGWu9evWKlNJyr3/sscd+9j7q168fo0ePXpVlAQAAAMDPWqPWWAMAAACAikKwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABpmCtd69e8esWbOW2l5YWBi9e/f+pTUBAAAAQIWXKVh75plnYtGiRUttX7BgQfznP//5xUUBAAAAQEVXeWUav/XWW7n/v/vuuzFt2rTc5eLi4hg7dmysu+66q646AAAAAKigVipY69y5c+Tl5UVeXt4yD/msUaNGXHvttausOAAAAACoqFYqWJsyZUqklKJNmzbx8ssvR6NGjXLXVa1aNRo3bhyVKlVa5UUCAAAAQEWzUsFaq1atIiKipKTkVykGAAAAANYUKxWsLemjjz6Kp59+OmbMmLFU0Hbeeef94sIAAAAAoCLLFKzdeOONcfzxx0fDhg2jadOmkZeXl7suLy9PsAYAAADAWi9TsHbJJZfE0KFD4+yzz17V9QAAAADAGiE/y42+++672G+//VZ1LQAAAACwxsgUrO23337x+OOPr+paAAAAAGCNkelQ0Hbt2sUf//jHeOmll6Jjx45RpUqVMteffPLJq6Q4AAAAAKioMgVrI0aMiNq1a8f48eNj/PjxZa7Ly8sTrAEAAACw1ssUrE2ZMmVV1wEAAAAAa5RMa6wBAAAAwG9dphlrRxxxxE9ef/PNN2cqBgAAAADWFJmCte+++67M5aKionjnnXdi1qxZ0bt371VSGAAAAABUZJmCtfvuu2+pbSUlJXH88cdH27Ztf3FRAAAAAFDRrbI11vLz82Pw4MFx1VVXraq7BAAAAIAKa5WevODjjz+OxYsXr8q7BAAAAIAKKdOhoIMHDy5zOaUUX331VTz88MMxcODAVVIYAAAAAFRkmYK1119/vczl/Pz8aNSoUVx55ZU/e8ZQAAAAAFgbZArWnn766VVdBwAAAACsUTIFa6W+/vrr+OCDDyIiYqONNopGjRqtkqIAAAAAoKLLdPKC77//Po444oho1qxZbL/99rH99ttH8+bN48gjj4x58+at6hoBAAAAoMLJFKwNHjw4xo8fHw899FDMmjUrZs2aFQ888ECMHz8+Tj/99FVdIwAAAABUOJkOBb3nnnvi7rvvjl69euW27brrrlGjRo3Yf//944YbblhV9QEAAABAhZRpxtq8efOiSZMmS21v3LixQ0EBAAAA+E3IFKx17949zj///FiwYEFu2/z58+PCCy+M7t27r7LiAAAAAKCiynQo6NVXXx0777xztGjRIjbbbLOIiHjzzTejWrVq8fjjj6/SAgEAAACgIsoUrHXs2DE++uijuO222+L999+PiIiDDjooDj744KhRo8YqLRAAAAAAKqJMwdqwYcOiSZMmcfTRR5fZfvPNN8fXX38dZ5999iopDgAAAAAqqkxrrP3973+PjTfeeKnt7du3j+HDh//iogAAAACgossUrE2bNi2aNWu21PZGjRrFV1999YuLAgAAAICKLlOw1rJly3j++eeX2v78889H8+bNf3FRAAAAAFDRZVpj7eijj45TTz01ioqKonfv3hER8eSTT8ZZZ50Vp59++iotEAAAAAAqokzB2plnnhkzZ86ME044IRYtWhQREdWrV4+zzz47hgwZskoLBAAAAICKKFOwlpeXF3/+85/jj3/8Y7z33ntRo0aN2GCDDaJatWqruj4AAAAAqJAyBWulateuHVtuueWqqgUAAAAA1hiZTl4AAAAAAL91gjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQQbkGa88++2zsscce0bx588jLy4v777+/zPUppTjvvPOiWbNmUaNGjejTp0989NFHZdp8++23cfDBB0dBQUHUq1cvjjzyyJg7d+5qfBQAAAAA/BaVa7D2/fffx2abbRbXXXfdMq+/7LLL4q9//WsMHz48JkyYELVq1Yq+ffvGggULcm0OPvjgmDRpUowbNy7GjBkTzz77bBxzzDGr6yEAAAAA8BtVuTx/+S677BK77LLLMq9LKcXVV18d5557buy1114REXHrrbdGkyZN4v77748DDzww3nvvvRg7dmy88sorscUWW0RExLXXXhu77rprXHHFFdG8efPV9lgAAAAA+G2psGusTZkyJaZNmxZ9+vTJbatbt25069YtXnzxxYiIePHFF6NevXq5UC0iok+fPpGfnx8TJkxY7TUDAAAA8NtRrjPWfsq0adMiIqJJkyZltjdp0iR33bRp06Jx48Zlrq9cuXLUr18/12ZZFi5cGAsXLsxdLiwsXFVlAwAAAPAbUWFnrP2ahg0bFnXr1s39tGzZsrxLAgAAAGANU2GDtaZNm0ZExPTp08tsnz59eu66pk2bxowZM8pcv3jx4vj2229zbZZlyJAhMXv27NzP559/voqrBwAAAGBtV2GDtfXXXz+aNm0aTz75ZG5bYWFhTJgwIbp37x4REd27d49Zs2bFxIkTc22eeuqpKCkpiW7dui33vqtVqxYFBQVlfgAAAABgZZTrGmtz586NyZMn5y5PmTIl3njjjahfv36st956ceqpp8Yll1wSG2ywQay//vrxxz/+MZo3bx79+vWLiIhNNtkkdt555zj66KNj+PDhUVRUFCeeeGIceOCBzggKAAAAwK+qXIO1V199NXbYYYfc5cGDB0dExMCBA+OWW26Js846K77//vs45phjYtasWdGjR48YO3ZsVK9ePXeb2267LU488cTYcccdIz8/P/r37x9//etfV/tjAQAAAOC3JS+llMq7iPJWWFgYdevWjdmzZ6/QYaFdz7x1NVTFkiZePqC8SwAAAAB+A1YmJ6qwa6wBAAAAQEUmWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZVOhg7YILLoi8vLwyPxtvvHHu+gULFsSgQYOiQYMGUbt27ejfv39Mnz69HCsGAAAA4LeiQgdrERHt27ePr776Kvfz3HPP5a477bTT4qGHHoq77rorxo8fH19++WXss88+5VgtAAAAAL8Vlcu7gJ9TuXLlaNq06VLbZ8+eHf/4xz9i9OjR0bt374iIGDlyZGyyySbx0ksvxdZbb726SwUAAADgN6TCz1j76KOPonnz5tGmTZs4+OCDY+rUqRERMXHixCgqKoo+ffrk2m688cax3nrrxYsvvviT97lw4cIoLCws8wMAAAAAK6NCB2vdunWLW265JcaOHRs33HBDTJkyJbbbbruYM2dOTJs2LapWrRr16tUrc5smTZrEtGnTfvJ+hw0bFnXr1s39tGzZ8ld8FAAAAACsjSr0oaC77LJL7v+dOnWKbt26RatWreLOO++MGjVqZL7fIUOGxODBg3OXCwsLhWsAAAAArJQKPWPtx+rVqxcbbrhhTJ48OZo2bRqLFi2KWbNmlWkzffr0Za7JtqRq1apFQUFBmR8AAAAAWBlrVLA2d+7c+Pjjj6NZs2bRtWvXqFKlSjz55JO56z/44IOYOnVqdO/evRyrBAAAAOC3oEIfCnrGGWfEHnvsEa1atYovv/wyzj///KhUqVIcdNBBUbdu3TjyyCNj8ODBUb9+/SgoKIiTTjopunfv7oygAAAAAPzqKnSw9sUXX8RBBx0UM2fOjEaNGkWPHj3ipZdeikaNGkVExFVXXRX5+fnRv3//WLhwYfTt2zeuv/76cq4aAAAAgN+CvJRSKu8iylthYWHUrVs3Zs+evULrrXU989bVUBVLmnj5gPIuAQAAAPgNWJmcaI1aYw0AAAAAKgrBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIQLAGAAAAABkI1gAAAAAgA8EaAAAAAGQgWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMBGsAAAAAkIFgDQAAAAAyEKwBAAAAQAaCNQAAAADIoHJ5FwAVQdczby3vEn5zJl4+oLxLAAAAgF/EjDUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABk4OQFwFrHyShWPyejAAAAfovMWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQgWANAAAAADIQrAEAAABABoI1AAAAAMhAsAYAAAAAGQjWAAAAACADwRoAAAAAZCBYAwAAAIAMKpd3AQDwU7qeeWt5l/CbM/HyAeVdAgAArBHMWAMAAACADARrAAAAAJCBYA0AAAAAMhCsAQAAAEAGgjUAAAAAyECwBgAAAAAZCNYAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQQeXyLgAA+O3oeuat5V3Cb87EyweUdwkAAGstM9YAAAAAIAPBGgAAAABkIFgDAAAAgAwEawAAAACQwVpz8oLrrrsuLr/88pg2bVpsttlmce2118ZWW21V3mUBAKzVnJBi9fs1T0ihP1c//bl2ccIY+O1ZK2as3XHHHTF48OA4//zz47XXXovNNtss+vbtGzNmzCjv0gAAAABYS60VM9b+8pe/xNFHHx2HH354REQMHz48Hn744bj55pvj97//fTlXBwAAAFCWWaWr368xq3SND9YWLVoUEydOjCFDhuS25efnR58+feLFF19c5m0WLlwYCxcuzF2ePXt2REQUFhau0O8sXjj/F1RMFivaN1np09Xv1+xT/bn66c+1i/5cu3gNXfsYo2sX/bl2+bX3uaxdjNHVb0XHaGm7lNLPts1LK9KqAvvyyy9j3XXXjRdeeCG6d++e237WWWfF+PHjY8KECUvd5oILLogLL7xwdZYJAAAAwBrk888/jxYtWvxkmzV+xloWQ4YMicGDB+cul5SUxLfffhsNGjSIvLy8cqzs11VYWBgtW7aMzz//PAoKCsq7HH4h/bl20Z9rH326dtGfaxf9uXbRn2sffbp20Z9rl99Kf6aUYs6cOdG8efOfbbvGB2sNGzaMSpUqxfTp08tsnz59ejRt2nSZt6lWrVpUq1atzLZ69er9WiVWOAUFBWv1APit0Z9rF/259tGnaxf9uXbRn2sX/bn20adrF/25dvkt9GfdunVXqN0af1bQqlWrRteuXePJJ5/MbSspKYknn3yyzKGhAAAAALAqrfEz1iIiBg8eHAMHDowtttgittpqq7j66qvj+++/z50lFAAAAABWtbUiWDvggAPi66+/jvPOOy+mTZsWnTt3jrFjx0aTJk3Ku7QKpVq1anH++ecvdRgsayb9uXbRn2sffbp20Z9rF/25dtGfax99unbRn2sX/bm0Nf6soAAAAABQHtb4NdYAAAAAoDwI1gAAAAAgA8EaAAAAAGQgWANW2MKFC8u7BH5F06dPL+8SAAAA1iiCNVap2267La666qryLoNfwb333huHH354fP755+VdCr+Cf/7zn3HIIYfEY489Vt6l8Cv44IMPYtq0aeVdBqvI888/H08++WR5l8Eq8vrrr8dbb71V3mWwikydOjWKi4vLuwxWIX26dvnmm28iIsI5HNcOFWV8CtZYZf7+97/HoYceGqeffnpceuml5V0Oq9CNN94Y++67b0ycODH++Mc/xhdffFHeJbEK3X///TFw4MB466234sorr4wnnniivEtiFRo+fHhssskmcfXVV8e3335b3uXwC1133XWx3XbbxeWXXx5PPfVUeZfDL3TDDTdE165d4y9/+Uu8++675V0Ov9CNN94YrVu3jhEjRpR3Kawi+nTt8o9//CMaN24cjzzySOTl5QnX1nAVaXwK1vjFUkpRUlISY8eOjdtvvz1GjRoV5513XgwdOrS8S2MVKCoqivnz58eDDz4Y5557bkyePDmGDBkiXFuLFBUVxVtvvRUfffRRzJw5M4YNGyZcW0s8//zzMXTo0DjqqKPiyiuvjCuuuEK4tgabMmVK3HLLLTFkyJAoKiqKa6+91sy1Ndhbb70Vf/3rX+Pkk0+O5557Lq666qqYNGlSeZdFRmPHjo0zzjgj+vXrF6eddloMHz68vEviF9Kna5e77rorjj766OjRo0fst99+wrU1XIUbnwl+oeLi4pRSSiUlJSmllObPn5+uv/76VKlSpXTJJZeUZ2msIgsXLsz9//rrr089evRIhxxySPr888/LsSp+DVOnTk2bb7556t27d3riiSfKuxx+oS+//DK98cYbKaWURo0alfLy8tKQIUPSzJkzy7kyspg3b1764osvUkopPfPMM2nbbbdN/fr1M1bXUN9++2368MMPU0op3X///ally5bpqKOOSu+88045V0YWU6ZMSS+++GJKKaVLL7005efnpxtuuKGcq+KX0Kdrl/feey89+eSTqaSkJJ1wwgmpSpUq6eGHH04p/e9zLGuOijY+BWtktnjx4jKXi4qKcv9fuHChcG0NtqwXl0WLFuX+f8MNN+TCtalTp67O0lgFSsfu4sWLy4zj0v8L19YOS47j0i9Abr31VuHaGqa070qVlJTk+nb8+PHCtTXQj19jSy8/8MADwrU1TGnfLet9U0X4oEd2P973pqRP10SlY/PH/fndd9+lQYMGCdfWUBVxfArWyKT0A/js2bPT8ccfXyZUKyVcWzOV9u2iRYvSjBkzlhucCdfWTKX9O2vWrNS9e/f05ptvLvN64dqaaVlvNFL64YuP0jeM//znP4Vra4jS8Th37tz0zDPPLPWFVkrCtTXJiozPBx98ULi2higdj/Pnz19um2HDhgli1iDLG6NLbtena47SMbrkkTdLEq6tWSr6+KxcvgeisiYqLi6OSpUqRWFhYXTo0CG23HLLqFz5f0+llFLk5eVF1apV48gjj4yIiJNOOikiIv7whz+US82smJRSrm/79esX06dPj/nz50fXrl1j2LBh0bp161xfH3fccRHxw5lgzznnnLj00kujZcuW5Vk+P2PJsdu5c+fYYIMNolOnTmXaVKpUKYqKiqJly5Zx//33R79+/eLSSy+NlFL06dOnnCpnRZT27/fffx///ve/Y9q0adG1a9fo0qVLNG3aNIqKiqJy5cpxyCGHRETEgAEDIiLijDPOiPr165dn6SzDkuN1vfXWi0GDBkXPnj1z15e+1m6//fZx6aWXxjnnnBN/+9vfIiJixx13LK+yWY6SkpLIz8+PuXPnxnXXXRfTp0+Ptm3bxu677x6tWrXK9fcee+wRERGDBg2KiIhTTz012rdvX56lswxLjs/mzZvHX/7ylzjmmGOWavf73/8+Iv7Xn6Xvnah4Svt03rx58fjjj8dXX30V2223XbRo0SLq1auXu16frhmWHKNdunSJYcOGxf7771+mTb169eKSSy6JiIh+/frF/fffH7vuumvu9ZWKY40Yn6s9ymONVpriz549O6233nqpX79+P3ub0plr+fn5Zq6tARYsWJC6dOmS9ttvv3T//fenf//732nDDTdMG2+8cbrnnnvSggULyrQ3c23NMnv27NSmTZu09957l9k2Y8aMNHfu3Ny20kN/l5y59uSTT672elk5hYWFqV27dmmbbbZJrVu3Tp06dUrt27dPb7/9dkrJzLU1xZKzwtdbb70y43VJS84WN3Ot4pszZ05q06ZN6t27d9pqq63SFltskRo0aJAef/zxpdqauVZxLTk+W7dunfbZZ59ltlty5otZThVbaV8VFhamjTbaKHXt2jWts846aaONNkp77rlnmjx5ckqp7DI4+rTi+vFr6LLG6JJ9aeZaxbamjE/BGittwYIFqVOnTmmLLbbIbbvqqqvSCSeckHbaaad05513LrWo/fz589N1112XatSokS666KLVXTIr4dVXX00bbLBBev/998ts32mnnVL79u3TmDFjUkpld17XX3996tOnT9pnn33SV199tVrrZcUtXrw4bbXVVmmdddbJbRs8eHD63e9+lxo0aJD22GOPdPfdd+euK/3Q/tlnn6Xtttsude/ePT311FOrvW5W3LHHHpt69+6d5s2bl4qLi9PTTz+d+vXrl2rXrp0mTJiQUvohNC19k7LkCQ2mT59enqXzI3PmzEmtW7dOe+65Z27bo48+moYPH55GjRqV+yJjybD06aefTttuu23q379/Gjt2bLnUzfKdccYZqUePHrnXz8mTJ6ejjz46Va1aNd1xxx0ppbL9WXpCg2OOOWapw/YpX3PmzEktWrRIhxxySG7bp59+ml5++eVUWFiYOzx0yUOUhg4dmvLz89Pw4cN9aK+AiouL00EHHZR23XXXNHPmzFRUVJRGjhyZ+vbtm9q1a5d7X7zk+199WnGVjtGDDz44t2369Om5flzWmmvHH398qlKlSnrkkUdSSsK1imRNGJ+CNVbaa6+9lnr37p26deuW3n777XTcccelzTbbLO27775p5513Ti1btkynnHJK+u9//7vUbffaa69UqVKl9Nlnn5VD5ayI//znP6lJkya5M5XNmzcvpfTDDq1Xr16pQ4cOac6cOSmlsrMldtttt9S6dWtv/iu4kSNHpurVq6c///nP6bDDDksdOnRIN9xwQ7riiivSwIEDU8OGDdN9992Xa1/6AnX++eenypUrp1deeaWcKufnFBcXp7322iudddZZZbZ//vnnaf/9909169ZNH3zwQUrph34tfVP52GOPpby8vHTllVcudx0SVr9///vfKS8vL910000ppZQOPfTQ1Llz59SyZcu08cYbp4YNG6aJEyemlMqGMR999FFq0qRJOuCAA8xErGAOP/zwdNhhh5XZVlxcnAYPHpyqVq2aO7tZUVFRbny+/PLLqXLlymnw4MG5117KV3FxcTr99NNTXl5emjVrVkoppWOOOSZ17tw5VapUKW200UbpjDPOSNOmTUsplT3hyIgRI1JeXl66/fbby61+lm3+/Plp++23T1deeWWZ7S+88ELaeeedU4cOHdKnn36aUtKnFd3ixYvTYYcdlvLy8nKfY0466aS0xRZbpOrVq6dOnTql66+/Pjd+S3377bfp3HPPTXl5eenZZ58tj9JZjjVhfOavvoNOWVt06dIlLrzwwmjZsmV069YtXnjhhbj77rtj9OjR8eijj8aZZ54Zo0ePjkmTJpW53bhx4+Kpp56KO++8M9Zbb71yqp6f07lz56hSpUpcddVVERFRo0aNWLhwYeTn58ejjz4aM2fOjEsvvTQiIrfe2gcffBAvvfRSXHbZZUut2UXFcthhh8WIESPi97//fbzwwgtx7733xnHHHRenn356nH/++dGzZ8+46667YsGCBRHxw5prH330UUyYMCFuu+222GKLLcr5EbA8+fn50bBhw3j22Wdj8eLFue0tWrSIYcOGRffu3eO0006LuXPnRqVKlXLrhxQXF0dExKabbhpVq1Ytl9pZ2oEHHhgXXXRRnHDCCbH55pvH5MmT48Ybb4w33ngjHnjggdhhhx1i1113jWnTpkXlypVz/TllypSYMWNGHHroodbOq2CaNGkSTz/9dMybNy8i/rfu2tChQ6N///5x5JFHxrffflumP7/99tsoLi6OHXfcMWrXrl2e5fP/5efnx5577hk77rhjbLPNNrHvvvvGW2+9Feeee268/fbbsd9++8Wzzz4bf/3rX2Px4sWRl5eX689q1apFRETdunXL8yGwDNWrV4969erFyy+/HCml3Pbu3bvH73//+2jQoEFcfvnlUVRUpE8ruEqVKkWvXr2iV69eccABB0T//v1jwoQJccIJJ8RTTz0VnTt3juHDh8ddd90VET/siyMi1llnnahXr15ERG4/TcWwRozPXz26Y62y5DTKZ555Jh133HHp3nvvTSmVnVK74YYbphNOOKHMbRcsWJD7NpaKqbQPR48enZo0aZKGDRuWu650JsshhxySDjrooDK3KywszB3fzpphzJgx6dprr00LFiwoM66PPfbY1KVLl6XO9PvFF1+s7hLJ4K677kpdunRJ11133VLrIY4cOTK1bds2dwhhSUlJmjt3bjrjjDPSQw89VB7lshw/PnxsnXXWWWqNw5dffjk1adIkd8hKSj/MdLr//vutsVZBTZgwIW255ZbpjDPOSIWFhSml//X1U089lVq3bp2bFVxSUpIWLFiQrrnmmvToo4+WW80s34QJE1Lv3r1Tu3bt0muvvVbmupNPPjltvPHG6fvvv08p/dCfhYWFufVrqZiuuOKK1KlTp/TII48sdajgBRdckDbZZJNcnxYXF+vTCqj0PW1JSUm6884705Zbbpnat2+f3nrrrTLt9tlnn7TVVlvlLhcXF6dZs2alrbfeOt1zzz2rtWZWTEUfn84KykrJy8vLnSmlZ8+e0bRp02jWrFlE/PANXnFxccydOzeaNm0am2++ee52xcXFUa1atdh6663Lq3RWQH7+D5NYd9xxxzjqqKPipptuisWLF8e5556bm8lSvXr1KCoqivTDoeSRn58fderUiTp16pRn6SwhLXE2o9IZET+22267xcKFC3Pf5JS2q1GjRnTr1i13+9L7WnfddVffAyCzvffeOx566KEYMWJE1KlTJ/bZZ5+oVatWRPzwrd78+fPjm2++iZYtW0ZeXl7UqlUrLrnkktzzgIohPz8/NybPOeec6NWrV2ywwQYR8b8xWbt27ahZs2aZb2ErV64ce+yxxzLHPOVvyy23jJ133jkeffTRqFOnTpx88sm52REbbbRRpJRizpw5EfHD+61q1arF8ccfH1WqVCnHqlmerbbaKi699NL46quvYqONNoqI/525bpttton77rsv5s+fHzVr1oy8vLyoU6dO3HLLLVGzZs1yrpzlOeWUU+LBBx+MM888M+rUqRPdunXLjb+dd945brzxxpg2bVq0adMm9/5Xn1YsS35W3XfffaNy5cpRVFQUG264YURELF68OCpXrhw77rhjXH311TFv3ryoWbNm5OfnR926dWPcuHFmB1dQFX18CtZYpuV9GI8ou8MqfSNRqlKlSvHAAw/EZ599VuaQwEqVKv2q9ZJNWs7ppBs3bhzHHHNMVK1aNa688sp4/fXXY7vttos5c+bEqFGj4sEHHywzzZaKo/RN/YIFC6KoqCjq1Kmz3H5eMkwpLi6Of/3rX3HLLbfEHXfckRuz+njNUVJSEpUqVYp//OMf0b9//7j66qvj008/jcGDB0f16tXjmWeeiapVqy51eKBQrWJaMlzbZpttcttLx+QzzzwTtWvXzn25teTtqHhK+/L888+P2bNnx8MPPxyffvppXH755VG5cuUYO3ZsFBUVLdWfQrWKqfR1tVu3bmW+pCp97Xzvvfdik002WWr/KoApf8t7T1RcXByVK1eOJ554IrbccssYNGhQnHXWWdG/f/+oXr16vPTSS1GnTp3cl1Wl9GnFs+Rn1b333jvmzZuXG4uly9h8+umn0bFjx6hUqVKZ54RQrWJaI8bnapkXxxqldLHyuXPnllnE/Oc89dRT6U9/+lOqWbNmuvPOO3+l6vglfjxtttTyzpQye/bsNH78+LTddtulbt26pV69euWeE86UU/GUjt05c+akzTbbLB188MFlzo6zPC+//HI666yzUp06dXJnptO/Fc/y+mTJ7aX9XVRUlE466aS0+eabp9q1a6devXql2rVr5/qX8re8/fHPjdmPP/44XXXVValmzZoOV6lASvttycOQfqy0zxcvXpyuvPLKtPXWW6f8/Py0+eabp3XWWcfi5xVI1tfA77//Pt14442poKAgPfzww6u4Kn6J0jG6vH1vSv87KdeiRYvSHnvskTthzF577ZVq1Kjh800FknWMFhUVpZtvvjnVrVvX2bMrkNLx+VPvgSr6+MxLaYnV3/jNS/8/sS8sLIxtt9021l133bj22mtzh6Asq22p66+/PkaNGhXnnntu7LHHHsv9RojyUTqT6fvvv4+rrroqJk+eHOutt17ss88+0blz52W2LZVSiuLi4liwYEHUrl07t2ik/q04SvussLAwOnToENOmTYtOnTrFs88+GzVr1vzJw0Ofe+65ePzxx2P77bePPn366N8KaMmZiBMmTIi5c+fGJptsEm3atFmqbelhDiml+Oijj+Lpp5+OgoKCaNOmTXTr1s2+uQL48f547ty5UVBQEOecc85P3u7TTz/NzSz985//HP3799efFcicOXPisMMOi5NOOil69eq1zDal4zMiYtasWfHkk09GQUFBNGrUKDp37qw/K4DS8VlUVBTz5s2LWbNmRatWrXLXL++ojgkTJsRNN90UDz30UPz1r3+N/fffX39WEEu+R9p1113jpptuio033niZbUvHaHFxcYwfPz6ef/75qFu3bnTp0iW22247fVoB/HiMzp07t8ySJcsbo//5z3/ihhtuiCeeeCKuvfbaOOCAA/RnBVDaX3PmzIlDDjkkjjnmmNhpp52WOWO7Io9PwRpLWbBgQWyzzTbRunXrGD16dFSvXn2pNksGL9OmTYumTZtGRMSXX34ZzZs398G8gindycyZMye22GKLaNeuXUT8cMablFKMGjWqzJvGUt9++22Zw8a8+FRMpS9IhYWF0alTp+jVq1dcccUVseGGG8bgwYPj3HPPXebtnnvuuejRo0dERG6NCWO34lnyDUfv3r0jPz8/Jk6cGDvvvHNceumlZQ67X5ExahyXryX3x1tttVU0bdo0KlWqFJMnT44BAwbERRddlGsXUXYsppTi7bffjqpVq8bGG29svFYwf/jDH2LYsGHRuXPnuOKKK6J3794rtOblkozP8rXk6+mee+4ZJSUl8frrr8d+++0X++yzT+y+++65dhFlD70uLi6Ov//977HZZpvFtttua3xWEEuGah07dozNN9887rvvvohYerwZoxXfkpNA9txzzygsLIwPP/wwDj744DjggAOid+/eEbH8MXrJJZfE9ttvHzvssIMxWgGU9ufcuXOjS5cusdFGG8Ull1wSm2222VLvf9L/X9t7Re6vPFiIg6W88cYbUa9evRg5cmRUr149rrnmmjj55JPjmGOOiUceeSQWLVqUC9X+8Y9/xAknnBDPP/98REQ0b948IsL6WxVMXl5eLFy4MPbdd9/o3Llz3H///fHwww/HeeedF99880189NFHS93m7rvvjvbt28ekSZPK3A8VT35+fsydOzc22mij2HLLLeOWW26JBg0axAEHHBBPP/10fP311/Hj71Beeuml2HfffWPw4MER8b81CIzdiqe0f7feeuto27Zt3HvvvTFhwoR44YUX4o033ljmbe644454/PHHl3md/i1feXl5UVRUFP3794/27dvHU089FXfffXfsueeeMW/evDLtSvvqqquuiuuvvz7y8vKiU6dOuZkWxmvFUrqYcteuXePUU0+NJ554osyJYEo/ENx0001xxx13LPM+9Gf5ys/Pj/nz5+dO0PWnP/0p7rnnnnjhhRfi5JNPjr/97W+5dqX9+fDDD8eECROiUqVKccIJJ8S2224bEcZnRZBSikqVKsWcOXOiQ4cOsdVWW5UJ1WbPnl2m/ZJ9+uqrry7zPvVp+Sr9TNOzZ89o0KBBXHzxxXHzzTfHSy+9FBdffHEMHz48IsqO0WeffTY+/PDDqFSpUpx//vm5UM0YLX+l6+GddNJJ0a5duxgzZkx07tw5vvzyy3jzzTfju+++y7Wr6ONTsMZSPvjgg5g6dWrUrVs3jjzyyBgxYkTMnz8/JkyYEOeff35ccsklUVRUFBERzZo1ixdffDFq1KhRzlXzc15++eUoLi6OU089NTe1docddoh11lknJkyYEBFRJnzp3LlztG3bNqZPn14u9bJyRowYEf369Yu77rorIn54YenXr1+MHz8+Xn311aVeaFq1ahVHH3107tt3Kq7i4uIYMmRItGnTJm6++eZo3rx5dO3aNfbcc8+YP39+jB49Op599tmI+KHfp0yZEiNGjIhzzjknZs6cuVSoSvn76quv4ttvv43TTz898vLyol69erHOOuvEpEmT4rjjjouTTz455s6dGxERhYWFMXXq1DjxxBNj6tSpuW/hqXh69OgRHTt2jKOPPjratWsXgwcPjnfffTfuu+++eO211yKlFN9880089dRTcdJJJ8W0adOMzwpozJgxUa1atbjppptim222iZ122imGDRsWn3/+edx4441x7bXX5tq+9957cfbZZ8ef/vSnmD17tv6sYEq/yNhnn31iwYIFufdI5557buy+++6xySabxIknnhjjx4/P3aa0T4cOHRqFhYXlVTo/4fXXX48FCxbEVVddFbvttlvsv//+ceedd0aLFi3i1ltvjZtvvjnXduLEidG/f//429/+ljtSJ0JAWpHk5eXF119/Hccee2xERBx22GGx3377RZcuXWLfffeNoUOH5tq+++67FXZ8OisoOaXJ/QYbbBDNmzePRx99ND788MO4++67Y5NNNomIiLPPPjseffTR2G+//aJjx46x6667xttvvx0NGzYs5+r5sR9PZ69bt27stdde0aVLl4j439T42rVr5z7ALfki07Zt23j88ceXWp+LiuHHfTJo0KDcGY9K+75v376x7777xmWXXRZbb711rLPOOrn2zZo1iz/+8Y9RtWpV/VvBLVy4MLbddts48MADo0aNGpGXlxf33HNP3HrrrfHFF1/Ep59+GjVr1ox+/frFRRddFOuvv36ceeaZUb9+/WjQoEF5l88yVK9ePf773//GPffcE927d4/bb789Lr744hg4cGBUrVo1HnnkkXj55ZfjpZdeioKCgjjjjDPi2GOPjfXWW6+8SyfK7n+X/H9eXl68/PLLcd1118WZZ54ZN954Y+y4444xffr0eOeddyIvLy8aNmwYZ555Zpx99tm5ZTSoWL755puYN29eVKlSJYqKiqJKlSrRtGnT6NGjRzRs2DDuvffe6NevX7Rs2TI22WSTOPPMM6Nt27ZRt27d8i6dZZg9e3Z06tQppk+fHldccUW8//778eabb8ZOO+0UO+ywQ4wcOTImT54clStXjm233bZMnxYUFJR3+SxD9erVY+bMmfHpp5/GeuutFyUlJbHRRhvFhRdeGOecc07ccccd0a1bt2jfvn107do1zjjjjNhiiy2cwbWCKioqis8++yy+/vrruPLKK+PNN9+Ma665JoqKiuLRRx+Nu+++O2rXrh2nnHJKbLrppnHGGWdEu3btKt74XEUnQWANtLyz4syYMSM1b948dejQIW211Vbpu+++y123aNGi1KBBg3T55Zfntjl7YMVTekaV77//Pj3xxBO57aVnU1myzwYOHJjOOeec3OV77703Pf/886upUrIoHbul/86ePTul9EO//ng8/v3vf08tWrRIb7/9dpnbsGaZM2dOblx/8MEHKS8vL/3lL39J8+bNSzNnzkxnnnlm6t69e/riiy/KuVJ+bFljbt68eenyyy9PdevWTT179kyVKlVKl112We76Tz75JNWpUyf985//XOq2XnPL15Jn3l20aFGZ677//vu0ww47pLlz56aUUjr66KNTlSpVUuvWrdOzzz672mvl5y1rPI0bNy5VqlQp3XXXXWnu3LlpwYIFaaONNkpDhgxJH3/8capevXr617/+VQ7VktWXX36ZLrjggtS4ceO06aabpg8++CC3b37nnXdSq1at0pAhQ8q5SlbUp59+mlq3bp3++Mc/ppR+GMel/Tlp0qTUoEGDMq+ppbx+lr9lvScqKSlJRx11VDryyCPTEUccUea9z/Tp09NRRx2V9t1339zn2IrKoaC/UcXFxZGfnx/ff/99XHnllXHKKafE6NGj47///W80atQoRo8enVt764MPPsjdrkqVKrHNNtvk1lKLMJW2oikpKckt0rrtttvGddddF5MnT46IyJ0p8Md9tmjRooiIGDVqVPTv3z++/vrr1V43K6Z07M6dOzeOPfbY2HXXXWPvvfeOp556aplrRRxzzDFRr1693ILoP7foJ+Xrx4f5lV6uXbt2bm3LDTfcMN5888047bTTonr16lG/fv3YaKONYurUqVG1atXVXjPLVzpeFyxYEI8//njceeed8c0330SNGjXimGOOiddeey2uueaa3OEOEf9boLdFixbRuHHjpe7Ta275KX19nTNnThx55JFx++23R3Fxce76mjVrRmFhYUyaNCn+/Oc/x7/+9a+4+OKLo0ePHjFw4MB48skny7F6fqy4uDjy8vKiuLg4vvvuu5g3b14sXrw4+vTpE+eee27sv//+0a1bt1h33XWjdevWMXTo0GjTpk1stdVW8f7770fE0vtsytfyXkObNWsWRx99dJxxxhlx/vnnR9u2bSM/Pz9KSkqiffv20bNnz3juueeipKTE4bwVyJL71yW1atUqzj333Ljkkkvi1ltvLTODeNNNN41+/frF008/nXs9LeX1s3wt+RnmiiuuiClTpuQ+lx500EExatSoGDlyZJlliBo3bhxbb711vPzyy7kjrCoqh4L+BqUlFvLccssto0WLFjFr1qwYN25cLF68OAYMGBDdu3ePq6++Oo455pgYMmRI/P73v4+NN944nn766Xj++efjD3/4Q3k/DJajdOHdbbfdNjbYYIO49dZbo3bt2rnrS19USk9X/O2330br1q3j4YcfjiOOOCJuu+222GuvvRweWAEtOXa7dOkSnTp1ii5dusSUKVPitNNOiyeffDIaNGiQ67fSw31POeWUuOyyy+KNN96Izp07l++DYLlKD+GdN29ejBw5Mvr37x9NmzZd5ljs2LFjRPxvPM+YMSO6du26zLM4Uz6W/JJjxx13jPnz58fXX38dlSpVitdffz2aNGkSBQUF8d1338X3338fEydOjPXXXz/y8vLihRdeiIULF5b5Eovylf7/yQdK978dOnSILbbYYql2nTt3jqOPPjo+++yzuOOOO2KPPfaI5557LhYsWCD4rkBKXx/nzJkTRx99dHz66aeRl5cXRx55ZBx++OFxwQUXRM+ePeOzzz6LatWqxUEHHRQREXPnzo2SkpJo3bp1RPiyqiIpfQ2dP39+jB07Nnr37h1169bNvYY2b948jjrqqKhcuXLui6rShdOLioqiW7du+rMCWfKLjMGDB8fMmTOjSpUqsfvuu0e/fv3iyCOPjI8++iiOOOKIWLRoURx++OG5/isuLo4WLVr4HFOBLPkZpmvXrtGuXbvYbbfdcuOzd+/eMWrUqDjkkEPiwQcfjJ49e+ZeY2fPnh3t27fPrRFeYa32OXJUCIsWLUp9+/ZNBx10UJo/f35KKaV99903HXXUUWXaPfvss2mDDTZI6623XmrdunVab7310h133FEeJbMSHn744bTjjjumhQsXppRSGjFiRDrvvPPSH/7wh/TWW2+VmUp77LHHplatWqXKlSvnpt4u65BCKoZ58+alHj16pAMPPDB3SNKYMWPSTjvtlBYsWJA7LHRJ77zzTqpTp0564403Vne5rKDS8TZ37tzUoUOHVLt27XTuueem6dOnl7l+WbcbOXJkqlu3bhozZsxqq5cVM3/+/NSlS5d06KGHpqlTp6Z33nkndezYMd166625Nt99913aZ599Uq9evdIhhxySzjrrrFSrVq105513lmPlLEtxcXE66qij0l577ZXb9umnn6Z33nknt++95557UsOGDdP9999f5raly2p4bS1/pX1QWFiYNtxww7TXXnulf/3rX6l///6pU6dOaebMmcu83eLFi9M//vGP1Lhx4/Tqq6+uzpL5GUu+hm688capfv366aabbkqFhYVlrl+WW265JTVp0iQ988wzq6VWVtzcuXPTBhtskHbdddd0zTXXpD333DNtuOGG6cADD8ztc88777yUn5+fjjzyyHT++eenP/3pT6lq1arpkUceKefq+bGFCxem3r17p7333ju3bdGiRWnBggW5z6ajRo1K1atXT7169UqHHnpo+v3vf59q1KiR7r333vIqe4WZsfYbNW3atPjyyy9zhxJFRGy22Wbx0UcfxSGHHBIdO3aMvn37xnbbbRevvPJKfPjhh7Fw4cJo3LhxbLjhhs6oUsFNnjw5Zs2aFVWrVo0BAwbExIkTo127dvHqq6/G448/Hscdd1wMHDgwKlWqFFWrVo2pU6fmFuPVtxXbW2+9FfXr148//vGPuW9cv/7663jvvfeiV69eMXfu3Dj++OPjhBNOiJRS7jCHjz/+OBo1alTO1bM8pYcjnXHGGdGsWbPYbrvt4pFHHomSkpI45ZRTonHjxkvNXBs/fnw88sgj8Y9//CNGjBhR5ps/Kobnn38+KleuHH/+85+jWbNmERHRrl27KCwsjBtvvDG22WabaN++fVx44YUxatSo3Nld77777th55531ZwVTetbdww47LCIijjvuuHjppZdi+vTpUalSpbjmmmuif//+0aNHj9xhvKV9WK9evdx9UL7y8vJi4cKF0a9fv+jYsWPcfvvtUbly5dhxxx2jX79+8d1330WVKlWiWrVquRP8lJ7d9dJLL42RI0dG165dy/thsIS8vLxYvHhxnHzyydGyZcvYdNNN4/LLL4+UUhxwwAFRp06dpW7z2GOPxdixY+Pmm2+OESNGRM+ePcuhcn7KpZdeGptuumncf//9ERFx8sknx1577RV33HFHzJw5M+6888648MILo2vXrnHTTTfFxIkTo1GjRnH77bfHLrvs4jW0gvn222+jqKgod5bPc889N954442YOXNmbLnllnHeeefFgAEDolWrVvHoo4/Giy++GJUrV4677rprjXiPK1j7jVq0aFEUFRXF22+/HVtssUU8++yzcf7558dRRx0V3333XTz55JPx2GOPxc033xytW7eOLbfcssztK/KT+rfmx2f/jIho2rRp1K1bN1566aWYPHlyPPDAA9GuXbsoKiqKgw8+OEaMGBF77LFHNGrUKPbYY484+OCDo1u3bkK1NUC3bt3iiiuuiPXXXz8iIh599NE4+uij4/e//31stNFG8dVXX8WJJ54YLVq0iD333DMXvpWGahX9Rem3bPHixbH++utHhw4dYtCgQXHBBRfEgw8+GBE/vJls0qRJmf5r3Lhx5Ofnx1133RU77LCDdWEqoJkzZ8brr7+e+wLr4Ycfjvvvvz+++uqrmD9/fpx44okxatSoOPDAA+Piiy+OatWqxcKFC6N69er6s4JJKUVhYWHMmTMnWrVqFSNGjIgXX3wxrrrqqqhRo0aMHj06/u///i/GjBkTv/vd73Kvzfa3FdN7770XXbp0iQEDBkTlyj98HHriiSfi3XffjT322CNq164d7dq1ixtuuCHq1q0bdevWjdq1a8eYMWOid+/eXksroNmzZ+e+mDrssMPiyCOPjMsvvzwiYpnhWqNGjeKLL76IO+64wxcZFdT06dNzh/MuWLAgatSoEbvsskssWrQoZs+eHRdeeGH86U9/ij333DP69OkT1atXj7lz50ZBQYHX0AqmpKQkpkyZEpMmTYpWrVrF2WefHY8++mgcc8wx8cEHH8TEiRNjl112ibFjx0bPnj1ju+22i/z8/NyZmdeI/lyd0+MoH8ub/nzccceltm3bpu233z7VqlUrXXXVVbnrHnnkkbTeeuulxx9//Cfvg/K15Nk/b7755jRv3ryUUkpffPFFql27durWrVvq06dP7gxlpW1r1KiRRo4cudT96eeKZcn+KO3rJRUXF6ehQ4cudXayrbbaKh1//PG/en38Mss6M9Ls2bPT999/n7v8xz/+MXXp0iUNGTKkzGGhpYfwl/5L+VvW/vObb75Jm2++eapdu3Y6/PDDU15eXrr++utzh7CcdtppqWnTpmnWrFmru1xWwpJ9u//++6euXbumAQMGpBtvvLFMu8MOOyxtvvnmS50tlPL34/3tokWL0qRJk3Kvrffff3/Ky8tLF154YfrPf/6Trr/++rT55punyy+/PHfbJfvV+6WKp6SkJE2ZMqXMe97DDjssbbjhhmnEiBFlDgtdsGBBSumHM26XbtOnFcfixYtTcXFxOuSQQ9L222+f+3yTUkqbbbZZuvjii9OQIUNS+/bt07fffptS+t+Y1I8VQ+m+dcl97/fff5+22WabNHTo0PS73/2uzNmyn3zyydS9e/f0l7/8ZanbrSms0LiWKz3j0aJFi+Ktt96K119/PaZNmxYRETfccEPcdttt8Ze//CVatmwZvXr1yt2uS5cuUb169dzZIn2DU/GULrxbWFgYm222WZxyyim5/lp33XXj2muvjY8++iimTp0as2bNyt0upRRdu3aNpk2bLnWf+rniWHLsRkRUqlRpqbMj5efnxznnnBMHH3xwbtvcuXOjbt26sdlmm63Welk5S54Z6cEHH4wFCxZERERBQUHUrFkzFi9eHBERF110Uey+++4xduzYuOaaa2LGjBlx/fXXx+677x5FRUUWQ68gSsdrUVFRfP311zF16tSIiKhfv37u8LFtttkm+vbtGwMHDszNYOvdu3fUrFkzvv/++/Isnx8pPZNg+v/fkP+/9u47rIrj6wP4dy+9NxUUBBQFRLpIANFYsaKgFHtLsXdsMSbGGhVrjDUWxAIidmNiVOwl9tglKvaCovTOef/gvRsuoKLJj7vg+TyPT2TLdW4OMzs7OzsnNzdX3DdkyBCoq6tj8+bNYv2T199GjRpBJpNVjCfrnxB5e5uRkYHt27cjMTERampqcHBwEGd1m5iY4JdffsF3330HX19fDBo0COrq6rh8+bL4VkDRhbO5v6RcpWVjFQQBVlZW0NHREftOa9euhY+PD8LDwxEdHY3U1FSsWrUKgwYNQl5eHrS1tcVzOabKUzyegiBAJpNh2rRpuHTpEj7//HP0798fVlZWMDIywrfffoupU6fi/v37OH/+vHhO0f8y5ZHfo6alpSEgIACXLl0CUHgvU6tWLUREROD27dsKCZqaN28OHR0d8diKmEik4pWYlVnRbCotWrRA//794eXlhUmTJuHevXsACl8rq1atGjQ1NRV+gffs2YOcnBzUrl1bWcVn71B0UM3JyQmWlpYwNzfH9OnTxWPat2+P8ePH48GDBxg7dixu3LiBly9fYseOHYiPj4epqakSvwF7F/r/zDlpaWlwcHBAaGgogNIH14rfwMXExOD27dulZqtj0lC0/lpYWODAgQPiQIs8nqqqqmJHUz649scffyAoKAjDhw9Hv379oKamViE7HpVN0Wtthw4d0LZtW9SpUwcLFy6EIAiwtLTEsGHDoKWlhXv37kFbW1sckImPj0eVKlWkn+nqE1J00HvcuHEICgpC//79xfXvPDw8xFcF58+fj8zMTLH+pqSkwNDQEFlZWTy4JhHy62lKSgrc3d2xe/duPH/+HMA/N/MFBQXw9fVF//79ART+DuTn58PW1haOjo7i5zBpkL9mnZaWhkmTJqFPnz744YcfcPr0aXFQRV1dXXxAJR9cW7BgAXr37o2BAweiXbt2UFVV5WuoBMjb3PT0dPzwww/o378/fvzxR1y7dg3W1ta4fPky7OzsIJPJ0Lt3b8TFxQEArl27hmrVqnH2bIkp2sdt2LAh9uzZgzNnzgAANDQ0sGjRImhqauLBgwfYsmWLwn2Ng4MDzM3NQUQVs81V0kw59j8mnwabmppK9vb2FBwcTDdu3KBNmzaRjo4OxcXFKRzfsGFDsrGxoZEjR9LIkSNJT0+PM5JJlDy2ycnJZGVlRSEhIZSfn0/Dhw8nLy8vMfMYEdGbN29o8+bNZGxsTJaWlmRvb0+mpqac2bUCyMrKIn9/f6pduzaZm5tTz549xX2lvRZ6+/Ztmjt3Lmlra3PdlTB57JKTk8nS0pK6dOlS6nHyel50KnzTpk1JEATavn27wjFM+dLS0qhevXrUtWtX2rlzJ82ePZvU1NTo77//Fo+5evUqubm50YABA+i3336juXPnkq6ubonskUx5SssW2b9/fwoICCBra2u6ePEiERVmZ16wYAFZWFhQrVq1aPz48TR06FDS1tamXbt2KfEbsNJkZmaSh4cHBQYGKrw+L29fS3sNcN26dVS9enU6fvx4uZaVlU1qairZ2NiQn58ftW/fnpo1a0aampq0cuVKheOK9pecnZ1JEASKjY0lIr6GSoG8Dsrb3LZt21K7du3Iy8uLwsLCxFdAS4vVihUrqH79+nTv3r3yLDJ7h6J9XAsLC+rTpw99+eWXZGtrq/AqfWJiIrm4uFDt2rVpzJgxdOjQIVqwYAHp6OjQgQMHlFX8f40H1iqx3Nxc6tmzJ3Xs2FFcS4CIqGPHjrRjxw46fvw4XblyhYgKG7bQ0FBq1KgRBQcH02+//UZEfNGRqoyMDFJTU6OQkBBx219//UUqKiol1nwhInr+/Dnt3LmT9uzZQ5cvXyYijq3Ubd++ndq1a0c7duygjRs3krm5OfXo0UPcL09LTUT05MkTWrhwIXl4ePCgSwWQmppKFhYW1KFDB3Hb1q1b6ccff6T58+fT2bNniUhxcG3OnDkkCII4CMPrwUjLzJkzqXHjxmKnMjExkVq1akUJCQkUHx9PRIXrM4WHh5OrqytVq1aNGjZsKKaP51hKR3Z2NnXo0IGCg4PFdvbmzZvk5eVFq1evFo/Lycmh8+fP01dffUXNmjWj0NBQ2rNnDxFxPKXm0KFD5OnpKa6xtXDhQhowYAD16tWL9u3bp3DsxYsXacGCBfyQSuJmzJhBnp6e4s36s2fPaPr06SSTyWjOnDkKx+bl5dG8efNIEATauXMnEfE1VErS09PJw8ODunXrRtnZ2URENHHiRAoMDCQixQFwIqL4+HiaNWsWaWhoUExMjHIKzd4qOTmZatSoQUFBQURU+FDR2tqali5dSkT/rFX56tUrGjx4MLm5uZG5uTm5ublV+HjywFollpiYSEuXLqWTJ0+K22JiYkgQBPL09KSaNWuSq6urwiL2ubm54iAcX3Skbe7cueLf5Z3/L774gpo3b04vXrwQ91XExR9Z4WzDdevWEVFhfDds2FBicK3ok9hHjx5RQkICEXHdlbojR46QIAj0/fffU15eHvXo0YNcXFzI0dGR3N3dSVNTU+xcyOM4YcIEsa3m+ErP4MGDyd/fX/x569atpKGhQW5ubqSpqUk9e/akJ0+eUF5eHr148YJu3bpFz549IyKOp9RcuHCBmjVrRn/88YfC9sDAQDEpTNEHG0SFMZS3xxxP6YmKiiIvLy8iIurWrRs1aNCAunXrRv7+/iQIAi1cuJCICmeKz5gxg5o3b64wAMOkZ+TIkdSuXbsS2xcvXkyCINCmTZvEbXl5efT111+LfSquo9ISFRVFgYGB9ODBA/GeZdGiRdS8eXPq0KEDDRgwgA4ePEhEhW3v/v37qWfPnvwgWYJyc3Opbdu24qAaUeFAW4sWLRQeJssH1/Ly8igjI4MePHhAiYmJRFSx6ycPrFVyL168EDt7f/75JwmCQOHh4fTq1Su6evUq9ezZkwICAsSMKkTcQEld8dcAi8YrKiqK9PT06NSpU0TEg2oVVWlxS09PL3Xm2u7du3kafAUUFRVFMpmMateuTY0aNaKLFy9SZmYmPXnyhEaPHk3a2tr0119/lTivInc4KrOVK1eSTCajb7/9liZPnkwqKio0b948unbtGh07dowMDQ3phx9+UHYxWRk8evSI1q5dK2bnlV9z+/XrR/379ycixeuuvE5yvZSuP/74gwwNDWnTpk3k5+dHt27dEvctWrSIBEEQX/nMzMykhw8fEhG3t1K2bNkyMjQ0pDt37hCRYkbIcePGkYWFhcKr+O967ZcpV0JCAp09e1aM0ZYtW0hNTY2GDh1Ko0aNou7du1P16tXFPlFeXp5CJlCOp7QcPnxY/Lv8+nnkyBFSV1dXWPqiMsZNIKqIK8Oxj3X48GGF7J/ffvstYmJicOnSJWhpaSmvYOw/4+fnB0EQsGfPHl4QuwKQL/IpJ1+UtzTp6enYuXMnxo0bh5YtW8LT0xNDhw7FyZMn4eXlVV5FZh+AiMTFlAsKChSyVm3ZsgUjR47EihUr4O/vL55z+/ZtNG3aFOHh4ejevbvCZzDlKh5PeV3Nzc3F/PnzcfbsWWRlZcHIyAiRkZHiMQMHDsSlS5dw5MgRaGhoKPMrsCLe1t7K2+Wi+8eOHYvExESsW7cOALBhwwZUr14dLVq0KM8is3d4W1v56tUrDBo0CE+ePEFGRgaOHz8OLS0tCIKAzMxM+Pr6olevXhg5cmT5F5q909vq6NWrVzFs2DDY2NhgypQpsLCwEON/8eJFBAQEYPXq1WjZsqUSSs3e5m3xzMvLg6qqKvLz89GlSxe0aNECw4YNA1CYpKBdu3aYPn06evXqVd5FZu/wtngWb4sTExMRGhoKe3t7/PzzzyCiSpk4pPJ9I1Yq+fipfFBNnglJEAR4e3tXyl/uT408xoGBgUhISMDt27eVXCL2PkWzCXbu3Bnx8fGQyWSlppEHAB0dHQQEBCA8PBwbNmzA0KFDsXHjRh5Uk6j8/HwIgoCcnBwAUIgtESEkJAQHDx4U4yevwzo6OjAyMoKJiQkATh0vFaXFU551Tk1NDePHj8fWrVthbm4Oc3Nz8RygsK7Xr18fqqqqyik8K0GeiS4jIwMLFy7EhAkTMG/ePAAoMagGFMZQHvuIiAj07t1bzC7JlE9eP7OysnD48GH8/vvvuHTpEgDAxMQEzZo1w8OHD3H9+nXcu3cPgiCAiKClpQVDQ0N+uCxBRevo/PnzMXnyZCxevBgA4OjoiMDAQFy8eBHh4eFISEgQr5W2trbQ1dVFSkqKMovPiikaz9WrV+Onn35CVFQUgH8yoauoqGD79u0YNmyY2F8yMjKCkZERqlSposzis2JKi2d0dDSAkv3WqlWromPHjoiIiMDdu3chk8kqZtbP9+Ae3iei+C+4TCZDREQEli5dik2bNvET9EpAHuMvvvgC33zzDZYsWYJly5YpuVTsXWQyGTIzM9G2bVucPHkS58+fx8GDB1GnTp23PgXS1tbG3bt3UVBQgN27d6N9+/bixYkHYKSDiKCiooK0tDS4urqiQYMGiI6OhoqKitgZAYB69eqJ58jjt3fvXuTl5aFWrVpKKTsr6W3xVFVVFZ+0y9WpUwdLlixBnz59UKVKFezbtw8xMTFi/JnyyeOZmpoKHx8fGBsbQxAEXLhwAZcuXUJkZKRYR+VP3lNSUmBsbIzffvsN/fv3x+bNmxEaGsozSiVAfkOekpKCFi1aQBAE3Lx5E/b29hg4cCD69++PQYMGITc3F7Nnz0ZISAhWr14NMzMzHDlyBFevXsWsWbOU/TVYEUVj6uPjAyMjI2hoaODYsWNITEzEtGnTMHz4cKSkpGD37t1ISEjADz/8AFNTU+zZswfPnz9HnTp1lP012P8rGk9fX1/o6uoiIyMDz58/x5s3bzBw4EDx4aO8PZW3wXv37kVOTg5q166tzK/AinhXPF+/fo2BAweKx8qvkf369UNMTAwWLlyIBQsWVM4HjeX53imThsOHD9OUKVPIwMCAoqOjiahyvuf8KZK/yx4eHl4i0xWTnoKCAlq4cCG1aNGCdu/eTW3atCFTU1O6ffs2EZW+1trx48fJwsJCXJiX15eQrqysLPL396fatWuTubk59ezZU9xXfK1EIqJbt27R7NmzSUdHh7Zu3VqeRWVlUNZ4nj9/XlwU3cXFhSwsLEoko2DKl5mZSY0bN6bOnTtTdnY25ebm0unTp8nQ0JB+//33EsdPnDiRdHV1SVVVlSIjI4mI218pSU9PJzc3NwoKCqInT57Q6dOnafDgwdSlSxdKTk4Wj9u8eTO1a9eOBEGgevXqUY0aNcS+MJOWtLQ0atCgAQUHB1NmZialpqZSWFgYDRw4UOG4yMhIMab169enqlWrUlRUlJJKzd4mLS2NXF1dKTg4mNLS0ujBgwcUGhpKkydPLvX458+f09KlS0lbW5tiY2PLubTsfT40nkREffv2JTs7O4U2uTKphEOF7H0KCgrw6NEjbNmyBX5+fpVyKuanSj4bYujQodDQ0OAn6RInCAKsrKwQHByM1q1bo0GDBujXrx+aNGmCo0ePom7duiVmrjk6OmLv3r1wdnbmmWoSt2/fPuTn52P+/PlIT0/HuHHj0LNnT2zYsAEqKioKM52ePn2KXbt2Yf369Vi/fj06d+7M9Vdi3hfP3NxcqKmpwd3dHatWrcLRo0ehoaGBWrVqwcnJia+1ErN7925kZ2dj7ty5UFdXBxGhbt26MDMzQ2pqaonj9fT0kJ6eju3bt6NTp07c/koIEWHJkiUwNjbGihUrYGxsjOrVq+P58+cIDQ3Fy5cvoa+vDwDo2rUrgoKCcOHCBairq0NbWxu2trbc3kpQeHg4DA0NsXLlSmhqagIofO0+ISEBAwcOhL6+PsLCwtCzZ08EBwfj3LlzUFNTg56eHurVq8cxlZgZM2agatWq+OWXX6CjowMdHR2YmZmJsw0NDQ0xYsQIAMCNGzewdu1a7N69m/tEEvUh8ZT3d2fPno1r166J7XFlw8kLKih54/KxjUxqair09PS4YygxxReyZ58G+Q05EeHRo0f46quvcOnSJRw7dkwcXLt9+zbMzMxgaGio7OKyMkpOTsaOHTvQp08f5OXlITo6GuPHj0fTpk2xYcMGAIp1/v79+8jPz0ft2rW5bZagssRTXpeZ9CUkJCA8PBzh4eHQ1NQUH2L4+voiNDQUw4YNU+hjXb9+HTk5OXB1deX6KTF5eXlYs2YNkpKSEBYWJj6wSEpKQoMGDbB37144ODi8MzkQk56kpCQcO3YMHTp0ENfe6tKlC4KDg2Fubo5NmzbB3t4eBw8e5L5zBXDr1i3cuXMHbdq0gUwmw7Zt2xAUFIT27dtDV1cXu3btQqdOnbBp0yYAwLFjx2BkZARHR0ducyXoQ+P5SbS/5To/jv1r8tdN5K8eFE0fzSo2eWzfvHlD8+bNo/T0dCWXiJWnonX4/v371Lp1azIzM6P4+HhavHgx2dvbU0JCghJLyD5EaW1yeno6bdy4kczNzalHjx7i9t27d9O9e/fKsXTsQ31IPPfs2UN///13eRaPfaB39aE+//xzmjt3rvjz8ePHS7S93OeSnoSEBMrMzCSif+KTmZlJNjY2dPHiRfG4W7duKaN47AMVXwrj2bNn1LlzZ1q+fLm4LSEhgQRBoJ07d5Z38di/dP/+ffLy8qKff/5ZjHVcXBxpampSXFyccgvHPti74nnkyBEll6788KugFUjRDIITJkzAmzdvAABjxoyBq6srj+JXYPJZKykpKahbty68vb0xevRoZReLlaOi9dfS0hKrVq3CoEGDYGtrC0EQsHbtWlhZWSmxhOxDlNYea2tro1OnTgCAcePGoW/fvvD09MTQoUNx8uRJWFtbl3MpWVl9TDxtbGzKu5isjIovji0IgjjbUBAEaGtrAwAiIyPRp08fHD9+XKH95f6W9MjjQ0Xe6EhOTsbr16/F7L0bNmzAV199hb/++gt16tThOEpY8ZktpqamWLx4sZhxGQDevHkDe3t7WFhYlHfx2L9kaWmJDRs2KFwniQgWFhYKMWYVw7viWb16dSWWrHzxwFoFIpPJkJ6eDldXV9ja2sLU1BRPnjxBw4YNMX36dPTu3VtsjIgIRCSms+XOg3QVzazi5OSEJk2aYOvWraUeKx+AKz6dlmNc+dSsWRMuLi749ddfsWvXLjH7J8e5YtPR0UFAQABkMhl69uyJ9evXY+PGjfDy8lJ20dhH4HhWHvJrakFBAXR0dLB9+3b0798fkZGR8PHxUXLpWFnJr5GCIEBdXR3q6uowMjLC1q1b0adPH0RGRqJu3bpKLiX7EPK+T40aNRS2nzlzBtra2jAyMlJSydjHkN/DFM/yeePGDZiamooPNljFwPH8Bw+sVTArVqyAiYkJ9u3bJ26bO3cufvzxR6SlpWH48OEwNTWFIAgQBAHnzp3Ds2fP0KFDB74plyiZTIasrCzY29vDyclJHFRbunQprl+/juzsbLi5uWHw4MFQUVERB9dOnz6NHTt24Mcff+S4VkI7duzArFmzsGnTJnFQjVUO2trauHv3LgoKCrB7926F+HJdrng4npWDfI0mIyMjzJ8/H1evXsX69evRo0cPjmcFpaOjA0tLS8yfPx/Lly/H+vXr0b17d45nBVN0sBQAnj17hr1792LEiBHYtGkTatWqpczisQ9UdKYwALx+/Rrbt29HWFgYNm/ezDPWKhiO5z94YK2Cyc7OhpqaGnJycqCiogIVFRWMHTsWGhoa+Pbbb1GzZk0MHDhQzL4xduxY3L59G48fP+YOhISlpKRAW1sbmZmZSElJwdChQ3Hu3Dk4OTkhMTERcXFxOHbsGDZv3gwVFRUQEa5du4Y5c+agdevWaNasmbK/AvuPaWhoIC4uDp9//jnfBFQyJ06cwLJly7Bx40YehKkEOJ6VQ0FBAQoKCvDq1StcuXIFO3fuhL+/P8ezAnv16hXOnz+Ps2fPYsuWLQgKCuJ4VnB37tzBsmXLEB0djcjISAQGBvLEgQrs6tWriIyMxPr16xERESFmXOZ4Vkyfejw5K2gFs2LFCowbNw63b9+GqakpcnJyoK6uDgD4/vvvER4ejhs3bsDS0hIAkJWVhSZNmmD8+PHo0qWLMovO3uPJkyfw8/PD9evX4ePjg1WrVqFevXrIzc1FVFQUvv/+e0yfPh3du3cHUDgYN2LECLRu3Rpdu3ZVculZUf/1ReRTuihVFP8mJqmpqUhISICTkxPf5FUCHM/K5dSpU0hNTYWfnx/Hs4JLT0/H2LFjERAQwPGUmI+9hr558waXL1+Gjo4OPDw8OKYV3KNHj3DmzBmYm5vDy8uL41nBferx5IG1CiY3NxctW7ZEbm4ufvvtN+jr6yMrKwuamppITU2Fq6srJk2ahP79+yMvLw9EhIiICPj7+8PU1FTZxWfv8ejRI4waNQpt27ZF//79xY5HamoqnJyc0Lt3b0ydOlU8fseOHXB3dxcHUpnyFV0HLzc3FxoaGiX2sYpLHkN53fzQ9OGfRLrxCuRtN3dlvenjeEqLvH7K+z9qamof/Vmf2g2BFBWvhx8zGJOSkgJ9fX2Op0T822sok5b/oo7y74B0FL9P4Xh+mE/zW1dgampqGD9+PPLz89G1a1ekpqZCU1MTQOE7zrq6uuLPqqqqUFNTQ79+/XhQrYKwsLDAypUr0blzZwAQOx1qamqwtbUV15EoKCgAAAQEBPCgmoQUzdzbr18/+Pn5oWvXrliwYAEAiGvksYpJ3uFITk6Gj48PEhMTP7jz8Kl2NqQoPz9fzAb54MEDJCQkICcnB8A/be/7cDylo2j9dHNzw9mzZ//V58nXqmXKIa+feXl5SE5ORlJSUokb+LLQ19cHwPGUgv/iGsqk47+qo/w7IA3y+pmSkoLg4OAS8SyrTzmen+43r8D8/PwwcuRIvHz5El5eXjh//jwuX76M2NhYPHjwoES2I54hIw1luUkDChdONjQ0FH+WyWSIiorCtWvX4OzsLG5j0iOTyZCRkQEPDw+8fPkSrVq1AhFhyZIlaNOmDQDFwbXivxNl/R1h5a9oh8PNzQ36+vqoWrXqe8/jSeHSRETiIHiTJk3QqVMn2NnZoXfv3oiKigJQWJ+L10mOpzQVr5/m5ualZvLk+FUMRetnq1at0KpVK9SuXRujRo3CoUOHABQOlBWPJ8dXuvgaWrlwHa1citZPJycn5Ofnw9jYGMC7Y8bxVMSvgkpI0emW73s9JT8/H2fPnsWsWbNw5MgRmJiYIDc3F3PmzOH1tiRI3mBlZGTg2LFj8PT0LFN68KtXr2Lbtm2YM2cO1q5di+Dg4HIoLfs3tm7dipkzZ+LgwYMwMjJCdnY2Dh8+jIEDB6JmzZo4evQogH9+J06cOIHz589j+PDhSi45ext5uyvvcLi7u2P79u3i/vT0dOjo6CgcDxR2KjMzM6GlpfVJT42XqpycHDRq1Ajm5ub45ptvEB8fj927d+P06dMYNmwYxowZA6BwwFs+24XjKV3yG3ZnZ2exfr58+RIZGRnQ09MTr7lF4/nq1SuYmJjwOpYSlJ2djYYNG6J27dr4+uuv8ffffyM6Ohr5+fkYPHgwevfuDUAxntnZ2dDQ0OB4SgxfQysnrqOVQ/H62aBBA2zbtk3cn5ubq7CsQtH6yfEshpgk5OXlERFRbm6uuC0/P/+dx8qdOnWKrly5Qrdu3SIiooKCAiooKPgflZR9KHkskpOTyc7Ojvr166cQq6LkMS8oKKDk5GT6+eefqXXr1rRjx45Sj2fSs3jxYqpevbrCtvz8fDp+/DhZWVlRUFCQuD03N5cmT55MWlpadPny5fIuKvsAOTk51KhRI6pZs6a4bdKkSRQYGEiWlpY0fvx4Onz4sMI5165dIycnJ7p582Z5F5eVwc2bN8nFxYWuXbsmbrtz5w59//33ZGxsTAsXLlQ4nuMpXfn5+dS8eXNSU1MTtw0bNoyaNm1KWlpa1Lx5c1qyZInCOXfv3iVtbW3av39/eReXlcHJkyfJ2dmZHj9+LG77888/6csvvyQHBwfauHGjwvF//fUX+fv708OHD8u7qKwM+Bpa+XAdrTyys7PJ09OT7OzsxG0zZ86knj17kqurKy1atEihr0TE8SwND/1LgHxdprS0NLRv3x6TJk0CUPprKEDJVzu9vLzg6OgIW1tbALyOhNQIgoCsrCw0a9YM9evXx08//aQQK7miT+MyMjKgr6+PkJAQrF27VkxXzKSvadOm0NDQwIYNG8RtMpkMnp6emDVrFq5fvy5Ok1dVVUXfvn3RpEkTJCUlKavIrAxev34Nb29vpKenIzIyEsOHD8fevXthZ2eHrl27Ii4uDjNmzMCJEyfEcx4/fgwdHR2cO3dOiSVnb0NEuHHjBm7fvi1uq127NgYMGIBBgwZhxYoV+OOPP8R9HE/pkslkCAsLg5aWFkaNGoW+ffviyJEj6NevH37++Wc4OTnhhx9+wMqVK8VzDA0N0bFjR2zevBnZ2dlKLD0rjUwmw507d3D37l1xW8OGDTFixAh4enpi9erVuHz5srjv7NmzuHPnDu7cuaOM4rL34Gto5cN1tPJ48OABrKysoKmpiX379mHgwIGIiYmBpqYmGjRogPDwcMyePRu3bt0Sz+F4lkLZI3usUEZGBrVq1Yr09fXJ29ubpk6dKu4rbebawoULafbs2eVZRPYv/PHHH/TZZ59RSkoKEREtW7aMBg8eTCNGjKDdu3crHLt582Zyd3enR48eKaOo7F968eIFBQYGUseOHen48eMK+5KSksjc3Jzmzp2rsH3VqlV0586d8iwm+whPnz6l7777jgRBoHr16tHff/8tts9HjhyhevXq0cyZMxXOGTZsGAUHByujuOw9kpOTqV27djRo0CB6+vSpwr7r16+Tj4+PwrWYiOMpdb///jupqqpSrVq1xJnhRERPnjyhfv36UYcOHSg1NVXcHhUVRV999RVlZ2cro7jsHe7evUtubm40c+ZMysrKUth37NgxsrS0pF9++UVhe2hoKPXs2bM8i8k+AF9DKxeuo5XLX3/9RV9//TXp6uqSo6MjxcfHi2/Sbdu2japUqUIREREK53A8FfHAmkSsXr2afHx8aOfOnTRq1Cj67LPPFDr0RV//zMvLo2nTppEgCHT37l1lFJd9oNWrV1OjRo2IiKh79+7k7u5OXbp0oTZt2pCamhotXrxYPDY6Oprc3NzoxIkTyiou+5fOnz9P9vb21LlzZzp06JDCvnbt2onxLv5aN5O+R48e0YIFC2jjxo2Ul5en8Hq2v78/tWjRgvLz8xUeiNy/f18ZRWVlsGrVKjIyMqL58+fTmzdvFPZ99dVX5OnpSTk5OQpx5nhK24kTJ2jp0qWUkZGhsH3ChAlkZ2dXYvvz58/Ls3jsA8ycOZPU1dUpNja2xL7AwEBq164d5efni9fS7OxsSkhIKO9isg/A19DKhetoxVe0Dl66dInGjh1LMTExJZaW8vX1FQe55QNuHE9FqsqeMccKBQYGIjMzEx07dsTnn3+OKVOmYO/evQCAyZMnQ0VFRXxVUEVFBcOGDUO3bt1Qq1YtJZeclYWVlRXu37+PtWvX4vHjx4iMjISDgwNSUlKwfPlyfPvtt3Bzc4Ovry9CQkLQpEkTmJmZKbvY7CMQEdzd3bF27VoMGjQI06dPx5kzZ9CmTRscPXoUcXFx+OabbwBwxl4povckkTE3N0fPnj2hqakpxq+goABEBG1tbbi4uIivdMsTVFhaWpbvl2DvJY/tl19+icePH2P8+PHIzc1FaGgorKysAAB6enqws7ODTCYTkwZxPKXPx8cHHh4eUFdXB/DPMgtEBC8vL4V6K5PJUK1aNWUWl5VCXj8nTpyIhw8fonfv3sjJyUH79u2hp6cHANDX14eBgYHY3hIR1NXVxfrLpImvoZUD19HKo2g/18XFBUZGRjA0NBS3FxQUIDs7G3p6evD09ARQuJQNx7MkHliTACKCkZERhgwZAgAwMDDApEmTMHPmTOzZswdA4eCaTCbDH3/8gVatWsHAwAAGBgbi+bymmjS8LWuRlZUVnJ2dsWXLFhAR7OzsABRedHr16oXIyEjcunULvr6+AMCDahJVlqxU8vTiXl5eiIyMxMqVK7Fs2TKsWLECampqiIiIQKNGjcqpxOxDyDvxOTk5yMjIgKGhYanHValSReFnmUyG9evX48iRIxg0aJC4nQdOletd10ZBEMT6/P3330NNTQ0//fQTjh07BltbW2hpaeHnn39GTEyMGEeOp3KVFs+3xVg+qAYUZn+Njo7G8uXLERUVJe7jDIPKVTx2RX+WX0cFQcDSpUuhrq6Ofv36oV+/frCysoJMJkNUVBR27Nghns/9YOl5W/3ka2jFULzPy3W0cnlb/Sw+kC2TyRATE4MrV65g8uTJ4naOZ0kCEa+IXp7kN25v+xn45xc9MTERs2bNwsmTJ+Hv7w8dHR2MHj0aZ8+ehbu7O/9CS4w8lunp6ViyZAkyMjJgbGyMESNGAAAWLVqECRMmQCaT4eTJk3BxcRHPbdasGXr16oX+/fsrq/jsPeTxzcjIwJ49e5CQkIAmTZqgXr164iC3XNHU4jk5OcjOzkZiYiK0tLRQvXp1hVTVTBrk8U1LS0NQUBDat2+PYcOGvXcw9fDhwzh48CAWLFiANWvWICQkpBxLzd5GHreCggJkZGRAU1MTqqolnyUWje+OHTtw4sQJ7N+/H3Z2dujRo4eYOIbrqnLJ62d2djYuX74MmUwGKysrVK1a9Z119OzZs9iyZQtWrVqFlStXIiQkhOMpAfKYZWdn4+7du6hXrx6Adw+Gr1y5EocOHcL58+dRq1YtDBw4EJ07d+Z4SkRp9zNy74oRX0OlqWifaNu2bejdu/d7z+E6Kl0fWz/379+PkydPIjw8nOtnGfDAWjmSdyTS0tIwduxYjB8/HtbW1qV2CuXbXr16hR9//BHr1q1DUlISNm7ciK5duyrpG7C3kTdKqamp+Oyzz1C1alUQEe7evYtBgwaJmV7nzZuH6dOnw9nZGdOmTYO9vT327NmDiRMn4tdff0WDBg2U/E1YaeT1MTU1FY0bN4aamhpevXqFpKQkTJkyBSNHjiy1HmdmZkJLS0tJpWZlJe9wpKSkwNnZGQ8ePEDjxo1x5MiREscWj/O+ffsQGRmJ3r17o02bNtyBlAB5PFNTUzFgwAA8fvwYmZmZmDRpEjp27PjWbMxy8gyRGhoaPAguAfIYpaSkoG3btnj9+jVSU1Ph5OSEdevWKbzKWTyeFy5cwB9//AFPT080a9aM4ykhWVlZ8PLygpWVFb755ht89tlnJY4pHs/MzEzk5+ejoKAA+vr6HE+JKNpHGj9+PJKTk5Gbm4v+/fvDy8sLhoaGCg87+BoqbUX7RI6OjujQoQOWLl1a6rFcR6WvaP0cO3YsUlJSAACDBw+Gi4sL9PT0FJZMKBqr2NhYLFmyBGFhYWjfvj3Xz/f5z1dtY++Unp5Onp6eJAgCNW/eXFzwr7TMn/IFA6dOnUqCINCuXbvE7UUXE2TSkJOTQ82aNaOgoCDKz8+nxMRE+uqrr+ibb75ROG7VqlXUsmVLEgSBHB0dydTUlKKjo5VUalZWGRkZ5OXlRV27dqXXr18TUeGirVWqVKHExMQSx2/cuJGCg4NL3cekQ76gbnJyMllaWlK3bt3o7NmzZG5uThs2bHjreVeuXBH/Lv994LZZ+eT//1NSUsjW1pb8/f1p7dq15OfnRzY2NgoL1xeN1V9//VXuZWVll56eTvXr16fQ0FC6desWrV+/nurXr08nT54Ujynaj/r999/Fv2dmZop/5/opHefOnSNNTU2qXbu22O7KFV/U/t69e0ooIfsQqampVKdOHWrTpg3NmjWLWrZsSTVq1KCBAweKWZeL1lG+hkqTPEbJyclkZWVFHTt2fOuxXEcrjrS0NKpduzY1a9aMRo4cSe7u7mRvb09hYWGl1s+iCQnk9zFcP9+PB9bKUV5eHo0dO5ZatmxJCxcupJYtW1KTJk3Exqi0wbUdO3aQIAjiwAv/UkvXnTt3qEGDBnTu3Dlx24QJE6hTp040fPhwCgsLE2P38uVLOnPmDJ0+fZpu3rxJRNzhl7q1a9dSu3bt6M6dO+K2N2/ekI2NTakZXGfPnk2Ojo508eLFciwl+xhv3rwhS0tLMdvRy5cvycPDg7744gsiKlk3d+zYQQ0aNKBly5aVup8pV1ZWFjVv3lx8yEFE9OLFC/Lx8aFHjx5RVlYWZWdni8fL47lixQplFZm9R0REBH3++ecKA6MtW7ak2NhYOnr0KD18+JCICvtR58+fJwsLC+rZsycRcf2UqqysLOrbty9t27aNbGxsKCgoSOw/paSkiMetX7+emjRpQvv27VNWUVkZjBkzhvz9/RW2+fn5kSAIFBwcTE+ePBG38zVU2tLT06lu3brUunVrcVt0dDQtXLiQpk2bRvfv36ecnBxxH9dR6ZLXrYULF1Ljxo0V6tr3339Pn332GX355ZcK2bHl8dyzZ4/CZ7D345Vby5GKigrq1q2LVq1aYejQoRgxYgRUVVXRp08fJCQkiFOki2rdujWOHj0qrgsC8HRaqdLQ0MDdu3exe/duAEB0dDRmz54NHR0dZGVlYePGjWjRogUAwMTEBJ6envjss8/ERAYcV2mhYm/J6+jooG7dugqLempqaiI7OxtPnjwpcX5YWBh27doFV1fX/3VR2b80ceJENGjQAFu2bAFQWD/HjBmDiIgInDhxokTddHR0hImJCZydnQFw3ZWaCxcuwMHBAVOnThVfUfn1119x9epVtG/fHj4+PggLC8Pz588BAPXr14eJiQkcHR2VWWz2DikpKbh58yZSU1MBANu3b8fBgwcxZcoUfP3113BwcFBYe2348OHimqVcP6WJiPDnn3/C0tISGzZswIULF7BkyRJ8/fXXaNWqlfhKtqurK54+fQojIyMll5i9y4sXL8TsgPJXzVq2bInGjRvjyZMnWLt2LfLz8wHwNVTqtm/fjtTUVHHtwwEDBmDGjBlYs2YNli5dihYtWmDHjh3Iy8sDwHVUyuR1Kz09Ha9evUJGRoa4b8qUKQgODsaVK1fwyy+/ICcnB8A/8ZQnGeH6+QGUPLBX6W3dupU2btyosK3oawk7duygFi1aUJMmTcRpl3l5efTq1asSn8UjxtJRWizS0tJo2rRppKWlRc2bNydBEGju3Lni/mvXrpGOjg7FxsaWZ1HZR5C/HpidnU23b98Wt2dlZRHRPzNH8/LyyN3dXeEp3fnz5+nWrVvlW2D2rxSdLZyfn08FBQX08OFDatSoEY0bN47y8/PFY+T/LTpzhknP5cuXxXq8Z88eEgSBJk+eTEeOHKEZM2aQi4sLrV27VmzLOZ7SUfT6Kq9vBw4cIG9vb3JxcaGwsDASBIEWLVpEz58/pwcPHlBISAh5eXmJr5Tl5uaW+CymHKXFQB7XwYMH09q1a4mI6NatW2RsbEzq6uo0Z84cheNTU1P/5+VkH0cey8DAQGrSpInCPhcXF1q8eDENGTKE6tatq9DOcpsrXfn5+RQeHk6+vr5kaWlJrq6udPXqVUpOTiYiIn9/f7KxsaGXL1+K53AdlSZ5+/vLL7+QnZ2deE8jv0YSEY0YMYJsbGwUZq1xPD8Oz1j7H3v+/DnWr1+PjIwMcQaMpqam+NSmU6dOGDZsGNTU1NCnTx/cv38fS5cuRZMmTcQns3I8YiwN+fn5YrbHa9eu4dy5c8jOzoaOjg6GDBmC8+fPY/bs2WjYsKFCogmZTAYLCwsYGxsrsfTsfQoKCsRFW1u2bIktW7bg4cOHAAB1dfUS6cbV1NTEc9euXYtWrVohPT1dKWVn7yefFSxvj/Py8sRZTUQEmUwGQRBgYWGBRo0aYd26dUhPTxcXdZUfq6mpqZwvwBRQsZml8murs7MzVFRUQERQU1NDREQEpk6diiZNmuCbb74BESEuLk6sy5xkRBrk19e8vDxkZWXh1atXAAozZ48ZMwYBAQFQV1dHSEgIhg8fjmrVqqFmzZpwd3dHVlYW1NXVAUDMAMv9JuWSxzMzMxPnzp1DVlYWAIjtqKmpKQ4ePAgA2Lt3L9LT02FsbIy//voLZ86cET9HR0en/AvPSlX8zRp5LGfOnInr16/Dw8MDQ4cOhbW1NQwMDDBs2DDMmzcPSUlJOHv2rHgeX0Olofg1VN4nGjVqFDp06AB7e3uEh4ejfv360NPTAwBs3boVDx8+xM6dO8XzuI5KQ/H6Kb8G9u3bFwUFBRg+fDiICKqqquKMw4ULF+Lly5eIjY0Vz+N4fhweWPsfc3V1RUpKCl68eAFBEMROv7zDD/wzuKahoQFvb2+MHj0aYWFhYgPGpKPooEubNm3QtWtXdO7cGW3btkVmZiaMjIxQr149WFhY4PXr17h8+bJ47tmzZ5Gfn6+QwYxJj0wmQ3p6Ojw9PWFoaIgvvvgCFhYWAAovUIIgoKCgQKzPSUlJyMvLw6ZNm/D1119j4cKFcHNzU/K3YKXJz88XMzNPnDgRwcHB+Pbbb8XOvrwDIu+YjB8/HgYGBvjxxx8V9hf/O1OOog85EhISAKBEOnlBEODn54devXoBKLxpyMnJgYODA9zd3cu7yOwd5NfX1NRUdO/eHS1btoSfnx82bNgAmUyGLl26YMqUKahatSqePHki9qfk51arVg05OTklbhSZchTtLzVs2BCrVq1CYmIiAIg3dPb29tDS0sJPP/2ESZMm4cCBA9i1axf279+PuXPnIjk5GQC3t1Ihv4amp6dj9uzZ+PLLL7F8+XLEx8fD3t4ex48fh5WVFVJSUtCtWzcxs/bly5ehra0NU1NT8bM4pspX9EHG69evARQ+lJDHedy4cZgwYQI8PDwAFMaMiHD//n3UqlULderUET+L46l8Revnxo0bMXPmTBw/fhzx8fFQUVFBdHQ0zp49i6CgIGRnZ4sPoNLS0mBra4uqVauKn8Xx/Diqyi5AZefj4wMtLS0MGTIEe/fuhYqKipjSVt5ACYKATp06ITY2Fn/88Qd27twJf39/TmkrMfLZKmlpafD09ISLiwtmz56NBw8eYMKECTh8+DDatm0LoPDmzs7ODosWLcKuXbtgYmKCxYsXY926dXBwcFDyN2FvI69zS5YsgZWVFXbt2gUA+P3338U1Q4KDg8UntIIgoEaNGlizZg127dqF9evXo3v37rweogQVvWn38PBA3bp1YWhoiBMnTuD169dwcnKChoYGBEEQ46ujowNfX18cOnQIeXl5YieEKR8RQUVFBWlpaWjevDns7e3xzTffwN7evtRj5XVRVVUV69evx/HjxzF8+PDyLjZ7B/n1tUGDBnBzc0NQUBBu376N2bNno2XLlqhatSpUVFRQo0YNZGRkICIiAo6Ojrh48SJmzJiBqKgoGBoaKvtrsP8nk8mQmZkJX19f2NvbY9asWeKMfXlb2qBBA/Tu3RtEhC1btsDX1xcAsG3bNuTm5sLAwEBp5WeKil5DPT09xQeOx44dQ1JSEr755hvY2dkhNja2xP3LhQsXYGxsDG1tbWUVnxVT9Brq5+eH5s2bY8yYMTAyMoKKigry8/OhoqKCZs2aKZwnCAKOHj0KVVVVhYFSplzyeMr7uGZmZsjMzMS6detgbW2N8ePHo0WLFtiyZQu6desGPz8/TJo0CTVq1MC5c+dw69YthTWk2Ucqv7dOPz3ydQeOHj1K7u7uCuttFV3Tp6CggNauXUuCIFBMTIy4jdcGkZ6cnBzq2LEjBQUFKWSVa9WqFW3fvp3i4uLE9fFOnjxJQ4cOJRcXFwoNDaW9e/cSEa/5UhEMGzaMRowYQUREoaGh5OrqSrVq1SIDAwPy8/MT15l48+YNOTg4kCAI4tp5XHelKyMjgzw9Palbt25i/Z0/fz61bt2acnNzFdpl+fpcf/75JwmCIGbvZdKRlZVF/v7+ZG1tTZaWljR48OB3xunMmTP0008/kZaWFm3ZsqUcS8rKoqCggMLCwqhNmzbi+i/Hjh2jFi1aUHJyMj19+pSICutmaGgo1alTh6pXr07169dX6Dsx6di6dSv5+vqK62lt2LCBJk2aRPPnz6djx44REdGiRYvo0KFD4jnF+8dMOtLT08nd3Z26du1K6enpRFSYDbRTp04KcZO7efMmhYeHk7q6ulhHmXRkZWVR69atydTUlNTU1GjKlCniOpWlOX/+PC1dupQ0NDRo69at5VdQVia5ubnUpUsX6t69u1g/d+3aRYaGhmRmZkY7d+4kIqI7d+6Qt7c32djYUPXq1cna2po2b96szKJXGvz4/X9IPuvB2dkZvr6+2L17N6pWrYo+ffpAJpOJTwPk60/s2LEDHTt25NkuEtesWTPY29uLa7nExsYiLi4Ojx49QkFBAV6+fImDBw/C29sbLi4uUFdXR05ODrS1tfkVFQmSzyAFINbJpKQkqKqq4tChQ7hz5w62bt0KdXV1JCUlISAgAL169cLOnTthYGCAXr16oX79+uIsU4DrrlQdPHgQVlZWmD59urg2nqamJl6/fo1WrVrB3NwcHTp0QNeuXcXZxU5OTvj7779Ru3ZtJZeeFXfx4kXk5uZi48aNePToEcaMGQMiwogRI8Rsy0X9+uuv2LdvH6KiosRrLddV6SAixMfHw8rKSpzR9PjxY1y5cgVNmzbF69ev0a1bN8ycORObNm3Cn3/+CRUVFZiYmKB27dp8fZWg+Ph46OnpQUtLCz169MC1a9dgZGSEjIwMLFiwAIsWLSoxc1R+PQb4Wio1W7duhY2NDebOnSuukWZmZobr168jICAA9evXR9OmTdG6dWsAwK1bt3DmzBlER0cjICCA21yJkb+qe+DAAZw8eRIDBw4EAIwYMaLE7N/s7Gxs2LAB27Ztw+bNmxEYGMjxlJi0tDQ8fPgQYWFh4uxQf39/NGrUCC9evMCkSZNgYmKCRo0a4eTJk7hy5QpycnJgYGCAOnXq8D3Mf0FZI3qfmgcPHlDnzp2padOmFB4eLm4v/oSHZ7tIT/HMrtnZ2eJsltOnT5OZmRnNnz+f7t27R2/evKE2bdrQZ599xrGsAORxTE9Pp0ePHonbf/31V3Jzc6M2bdrQgAEDFM45fPgwVa9enc6cOaOwneMtfU+fPqULFy6I7e6WLVtIXV2dRo8eTT/88AMNHjyYqlSpQkePHi31fI6vchX////q1Ss6cOCAOBsmOjqaLCwsaNCgQXTjxg3xuKIZXe/duyd+FsdTer777juysrKiZcuW0eLFi0lNTY1+/PFH2r17N61fv54EQaDly5cru5isjKKjo8nNzY3WrVtHXl5edO3aNSIiio+Pp5EjR1K9evXEbUx6ireRN27coJMnT4pt6ubNm0lNTY0GDhxIQ4YMocDAQHJ1daUrV66I5yQmJoqfxW2utNy9e5e2b99OmZmZRES0YsUKEgSBpkyZQklJSSWOT01NpevXrxMRx1NqCgoKKCEhgWxsbCgiIkLcnpGRQR4eHjRz5kzy9PSkb775RomlrPx4xlo5qVmzJhYsWIC5c+ciKioKhw4dwpo1a6CjowNdXV1x1J9HiaXn+fPn2LVrFwICAqCtrS3OVAMAExMTREZGomXLluI2b29v7Ny5E3l5eQoZI5m0yGenpaWlwc7ODiYmJrh48SJUVFTg4OAAGxsb7N69G506dVI4T1dXF9ra2iWSi3DdlRYq8iRVPivRzMwMZmZm4jH79+/HvHnzMHToUADAvXv38Ntvv+HKlSto3Lhxic/kGCuPvL7m5uYiNzcX2traMDY2RosWLQAUxjgkJASCIGDUqFEAIM5ci46ORrVq1dCiRQtYW1sD4FgqW9GZwkV17NgRSUlJiIqKQnZ2NoYNG4bx48eL+2NiYnDo0CEMGDCAZ0tIyNtiUbNmTaipqWH37t0wNzcX15itU6cO+vTpg23btiEhIYHXnpUgeZubk5ODBw8eoE6dOrC3txdjnZ+fj5iYGMyePRsjR46EIAg4c+YMOnbsiPj4eDg6OgIAqlSpAoDbXGUrrY7WqlULNWvWhKqqKgoKCvD1119DEAQMGDAAwD8z1/bs2QMzMzN4eHigXr16ADieylb8GioIAqysrNCkSROMHDkSKSkpsLCwwLhx42Bra4uJEydCEASsW7cOU6dOLZHoif03eGCtHFlaWmLatGm4cuUKJk2ahICAAGhpaWHKlCnw9vbmQRiJcnV1xYYNG/DixQtYW1uLnQ0iQp06dcSsOPKLVmZmJpycnEBE3PGXKHkMU1JS4OTkBHV1dRARjh07hqZNm8LKygrjxo3DkydPsG3bNsyZMwfjxo0DEeHmzZvQ1tbmVPESJo+vPOFA0VfvgX/q6qpVqxR+NjAwQNWqVXlBXomRxy45ORnOzs5Yvny5mChGTt7OBgcHAwBGjx4NFRUVaGpqYuHChdi7d2+5l5uVTh7P9PR0zJgxA2/evIGuri7mzJkjJi7IyclBp06dxIFweR3V1NREzZo1AfCNnVQUHYBJSEhASkoKbG1toa+vD29vb7Rs2RKzZs2Cubk57t27h1q1agEAHBwcYGZmJmYIZdJRNKNrhw4d0Lp1a/Tp0wcWFhZi4jUVFRXExMRAJpOJmdItLCxgZmYGfX19ZX8FVoS8jmZlZeHcuXPIzc2FtbU1atWqBVVVVeTl5Yn9o6+++goAMGDAAMhkMhARZs+ejf379yvzK7AiisbzwIEDyM7ORo0aNeDt7Y01a9ZARUUFCxYsgIaGBnx8fLBu3ToAhYPc8vsd9j9S3lPk2D+OHTtGv/zyC61evVqchsukqXnz5tSuXTvx59IWaSUiWrduHRkaGtL+/fvLq2jsA8ljl5ycTFZWVtS9e3dKS0sjW1tb6tevn8KxFy5coB49epCenh45OjpS69atSVdXlxc+lzB5fFNTU8nPz09h2ntpr94XtWrVKqpTp47CayxMueSvaycnJ1OtWrWobdu2pR6Xn5+vEM/Y2FhSUVEhQRAoOjq6XMrK3k8eo5SUFLK3t6fmzZtTcHAwGRgY0ODBgxWOHT16NHXr1o3i4+MpJyeH1q5dSyYmJhQXF6eEkrPSyNvUlJQU8vX1JQ8PDxIEgUJCQhQSEowfP54EQaDevXvTpUuXKDMzk1avXk01atTg9lai0tLSyM7Ojvz9/enp06clrpe5ubklti1fvpwcHBzo9u3b5VlU9g5F29z69euTq6srqaqqUsOGDWn06NHicfn5+Qp9pNWrV5MgCCQIAm3atKncy81KV/Qexs3Njby8vMjQ0JA+//xz2rdvn3jckydPxIQ/cpMmTaJOnTpRRkbGW+9j2b/DA2tKUPxCxO+oS1dZM7teunSJJkyYQIaGhuJNHMdVutLS0sjQ0JCCg4PFbWvXrqXq1avTiRMnFI599uwZnThxgkaOHEmLFi2iI0eOEBHHV8oyMjKoVatWpK+vT97e3jR16lRxX2mdicePH9OSJUtIW1ubM11JUEpKCtWuXZs6d+4sbnv48CFdvXqVnjx5Im7Ly8sT6+WyZctIVVWV9uzZQ0S8HoyU5ObmUrt27ahLly5EVJiZbsaMGfT1118rHLdx40by9PSk6tWr0+eff07VqlXjQVIJSktLI2dnZwoNDaXLly/T77//Tk5OTjRq1CiF46ZPn05ubm6kqalJDRs2JGNjY46nBMnbyR9//JH8/PzE7b/99htt2bKl1AeLiYmJtGLFCtLW1qZt27aVW1lZ2eTm5lLLli3FQdLz58/TnDlzqEqVKhQUFCQeV/QaumrVKlJRUaG9e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<p>The above plot generates a countplot to visualize the distribution of the number of house transactions in the "houseprice_melbourne" dataset.
The The countplot generated is a bar chart that shows the number of houses that have a certain number of transactions. The x-axis shows the transaction count bins, and the y-axis shows the number of houses in each bin and from the plot there seem to be more houses with a transaction count between 1137 and 1513 compared to houses with a transaction count between 250 and 500.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [42]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">num_bins</span> <span class="o">=</span> <span class="mi">10</span>
<span class="c1"># Define the bin edges using numpy's linspace function</span>
<span class="n">bin_edges</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">houseprice_melbourne</span><span class="p">[</span><span class="s1">'Median_Price'</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">houseprice_melbourne</span><span class="p">[</span><span class="s1">'Median_Price'</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">num_bins</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># Create the count plot with specified bins</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">houseprice_melbourne</span><span class="p">[</span><span class="s1">'Median_Price'</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><span class="n">bin_edges</span><span class="p">),</span> <span class="n">data</span><span class="o">=</span><span class="n">houseprice_melbourne</span><span class="p">)</span>
<span class="c1"># Rotate x-axis labels for better visibility if necessary</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Count of Median Prices by Bins of Median Price"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>The above plot represents the countplot visualizing the distribution of median house prices in the "houseprice_melbourne" dataset.
The distribution of the median house price appears to be right-skewed. This means that most houses in the dataset are priced below a certain threshold, but there are also some very expensive houses and this plot can be useful in understanding the general price range of houses in the area and identifying potential outliers.</p>
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<h3>Combining all Visualisations and Data</h3>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [43]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">houseprice_melbourne</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
</pre></div>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[43]:</div>
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<table border="1" class="dataframe">
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<tr style="text-align: right;">
<th></th>
<th>Year</th>
<th>Region</th>
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<th>Median_Price</th>
<th>Transaction_Count</th>
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<tbody>
<tr>
<th>0</th>
<td>2000</td>
<td>Carlton</td>
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<th>1</th>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [44]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">melbourne_job</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
</pre></div>
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<th>383</th>
<td>2016-01-01</td>
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<td>1045.0</td>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [45]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Convert 'Year' column to integer type in melbourne_job dataframe</span>
<span class="n">melbourne_job</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">melbourne_job</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">])</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">year</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
<span class="c1"># Merge the two dataframes based on "Year" and "Region"</span>
<span class="n">new_dataset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">houseprice_melbourne</span><span class="p">,</span> <span class="n">melbourne_job</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="p">[</span><span class="s2">"Year"</span><span class="p">,</span> <span class="s2">"Region"</span><span class="p">],</span> <span class="n">how</span><span class="o">=</span><span class="s2">"inner"</span><span class="p">)</span>
<span class="c1"># Display the merged</span>
<span class="n">new_dataset</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
</pre></div>
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<th>Region</th>
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<th>Transaction_Count</th>
<th>Total jobs in block</th>
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<th>0</th>
<td>2002</td>
<td>Carlton</td>
<td>Residential Apartment</td>
<td>349250.0</td>
<td>297</td>
<td>2166.0</td>
</tr>
<tr>
<th>1</th>
<td>2002</td>
<td>Carlton</td>
<td>Residential Apartment</td>
<td>349250.0</td>
<td>297</td>
<td>381.0</td>
</tr>
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<th>2</th>
<td>2002</td>
<td>Carlton</td>
<td>Residential Apartment</td>
<td>349250.0</td>
<td>297</td>
<td>203.0</td>
</tr>
</tbody>
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<p>Combining all the data successfully merged the houseprice_melbourne and melbourne_job datasets based on the "Year" and "Region" columns.The new_dataset contains good information that allows to analyze the relationship between house prices and job availability in different regions of Melbourne across various years.Now by visualizing and analyzing the merged data, we can gain insights into the housing market and job market dynamics in Melbourne.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [46]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">new_dataset_encoded</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">new_dataset</span><span class="p">)</span>
<span class="c1"># Create a correlation matrix</span>
<span class="n">correlation_matrix</span> <span class="o">=</span> <span class="n">new_dataset_encoded</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
<span class="c1"># Plot the heatmap</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">correlation_matrix</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'coolwarm'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Correlation Heatmap of new_dataset"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<p>The heatmap shows the correlation coefficients between various features in the merged dataset, new_dataset_encoded.</p>
<ul>
<li>There is positive correlation between 'Median_Price' and 'Transaction_Count'. This suggests that regions with higher median house prices tend to have a higher number of property transactions.</li>
<li>There seems to be a positive correlation between 'Total jobs in block' and 'Transaction_Count' as well.It indicates that areas with more job opportunities might also have a higher number of property transactions.</li>
<li>and it appears to be a weak negative correlation between 'Median_Price' and some of the region variables, like 'Region_Carlton' and 'Region_North Melbourne'. It says that these regions have lower median house prices compared to other regions.</li>
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<h2>Data Modelling</h2>
<h3>Linear Regression Model</h3>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [47]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">encode</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">new_dataset</span><span class="p">[</span><span class="s1">'House_type'</span><span class="p">])</span>
<span class="n">carpet</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">encode</span><span class="o">.</span><span class="n">classes_</span><span class="p">)}</span>
<span class="n">carpet</span>
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<pre>{'House/Townhouse': 0, 'Residential Apartment': 1}</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Enumerate region</span>
<span class="n">encoder</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">new_dataset</span><span class="p">[</span><span class="s1">'Region'</span><span class="p">])</span>
<span class="n">carp</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">encoder</span><span class="o">.</span><span class="n">classes_</span><span class="p">)}</span>
<span class="n">carp</span>
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<pre>{'Carlton': 0,
'Docklands': 1,
'East Melbourne': 2,
'Kensington': 3,
'Melbourne (CBD)': 4,
'Melbourne (Remainder)': 5,
'North Melbourne': 6,
'Parkville': 7,
'South Yarra': 8,
'Southbank': 9,
'West Melbourne (Residential)': 10}</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Convert to numerical variable</span>
<span class="n">new_dataset</span><span class="p">[</span><span class="s1">'House_type'</span><span class="p">]</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">new_dataset</span><span class="p">[</span><span class="s1">'House_type'</span><span class="p">])</span>
<span class="n">new_dataset</span><span class="p">[</span><span class="s1">'House_type'</span><span class="p">]</span>
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<pre>0 1
1 1
2 1
3 1
4 1
..
11009 1
11010 1
11011 1
11012 1
11013 1
Name: House_type, Length: 11014, dtype: int64</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">new_dataset</span><span class="p">[</span><span class="s1">'Region'</span><span class="p">]</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">new_dataset</span><span class="p">[</span><span class="s1">'Region'</span><span class="p">])</span>
<span class="n">new_dataset</span><span class="p">[</span><span class="s1">'Region'</span><span class="p">]</span>
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<pre>0 0
1 0
2 0
3 0
4 0
..
11009 9
11010 9
11011 9
11012 9
11013 9
Name: Region, Length: 11014, dtype: int64</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Convert categorical data to numeric and separate target feature for training data</span>
<span class="n">new_dataset</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s1">'Median_Price'</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">new_dataset</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span> <span class="s1">'Median_Price'</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">new_dataset</span><span class="p">[</span><span class="s1">'Median_Price'</span><span class="p">]</span>
<span class="n">y</span>
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<pre>0 349250.0
1 349250.0
2 349250.0
3 349250.0
4 349250.0
...
11009 565000.0
11010 565000.0
11011 565000.0
11012 565000.0
11013 565000.0
Name: Median_Price, Length: 11014, dtype: float64</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">X</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre><class 'pandas.core.frame.DataFrame'>
RangeIndex: 11014 entries, 0 to 11013
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Year 11014 non-null int64
1 Region 11014 non-null int64
2 House_type 11014 non-null int64
3 Transaction_Count 11014 non-null int64
4 Total jobs in block 11014 non-null float64
dtypes: float64(1), int64(4)
memory usage: 430.4 KB
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># split data</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span> <span class="o">=</span> <span class="mi">42</span><span class="p">)</span>
<span class="c1">#Scala data</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_train_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">SimpleImputer</span>
<span class="c1"># Impute missing values in the training data</span>
<span class="n">imputer</span> <span class="o">=</span> <span class="n">SimpleImputer</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s1">'mean'</span><span class="p">)</span>
<span class="n">X_train_imputed</span> <span class="o">=</span> <span class="n">imputer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train_scaled</span><span class="p">)</span>
<span class="n">X_test_imputed</span> <span class="o">=</span> <span class="n">imputer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test_scaled</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Fit the Linear Regression model with data</span>
<span class="n">model_lr</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_imputed</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">model_lr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_imputed</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># Calculate training and testing scores</span>
<span class="n">training_score</span> <span class="o">=</span> <span class="n">model_lr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train_imputed</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">testing_score</span> <span class="o">=</span> <span class="n">model_lr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test_imputed</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="c1"># Testing</span>
<span class="n">y_train_pred</span> <span class="o">=</span> <span class="n">model_lr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train_scaled</span> <span class="p">)</span>
<span class="n">y_test_pred</span> <span class="o">=</span> <span class="n">model_lr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_scaled</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Model: Linear Regression"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Training Score: </span><span class="si">{</span><span class="n">training_score</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Testing Score: </span><span class="si">{</span><span class="n">testing_score</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Model: Linear Regression
Training Score: 0.5504410100472327
Testing Score: 0.5469376003984241
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># Residual plot</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
<span class="n">y_train_pred</span><span class="p">,</span>
<span class="n">y_train_pred</span> <span class="o">-</span> <span class="n">y_train</span><span class="p">,</span>
<span class="n">c</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">,</span>
<span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s1">'Training data'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
<span class="n">y_test_pred</span><span class="p">,</span> <span class="n">y_test_pred</span> <span class="o">-</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">'green'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'s'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Test data'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Predicted values'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Residuals'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper left'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">)</span> <span class="c1"># Add horizontal line at y=0</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Residual Plot'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [57]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># Predict on the test data</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">model_lr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_imputed</span><span class="p">)</span>
<span class="c1"># Plot predicted vs actual values</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Actual vs Predicted'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">y_test</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">y_test</span><span class="o">.</span><span class="n">max</span><span class="p">()],</span> <span class="p">[</span><span class="n">y_test</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">y_test</span><span class="o">.</span><span class="n">max</span><span class="p">()],</span> <span class="s1">'k--'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Ideal Line'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Actual Values'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Predicted Values'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Linear Regression Model: Actual vs Predicted'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre><ipython-input-57-b28921d15ec8>:9: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string "k--" (-> color='k'). The keyword argument will take precedence.
plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2, color='red', label='Ideal Line')
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<p>From the above code performed data preprocessing steps and to trains a linear regression model to predict median house prices in Melbourne.</p>
<ul>
<li>By encoding the categorical variables "House_type" and "Region" using LabelEncoder by converting the textual categories into numerical values, making them suitable for numerical analysis and machine learning models.</li>
<li>Defined the target variable as "Median_Price" which you want to predict and The x matrix contains all the features that will be used to predict the median house price.</li>
<li>By splitting the data into training and testing sets. The training set is used to train the machine learning model, and the testing set is used to evaluate the model's performance on unseen data.</li>
<li>Performed scaling the numerical features in the training data and created a linear regression model using LinearRegression and fit it to the training data</li>
<li>The results score show that the model achieved a training score of 0.5504 and a testing score of 0.5469. This indicates that the model has a moderate ability to predict median house prices based on the provided features.</li>
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<h2>Randon Forest Model</h2>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">SimpleImputer</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
<span class="c1"># Assuming X and y are your feature matrix and target vector respectively</span>
<span class="n">model_rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="s1">'squared_error'</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_imputed</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Calculate the training and testing score</span>
<span class="n">training_score</span> <span class="o">=</span> <span class="n">model_rf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train_imputed</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">testing_score</span> <span class="o">=</span> <span class="n">model_rf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test_imputed</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="c1"># Testing</span>
<span class="n">y_train_pred</span> <span class="o">=</span> <span class="n">model_rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train_scaled</span> <span class="p">)</span>
<span class="n">y_test_pred</span> <span class="o">=</span> <span class="n">model_rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_scaled</span><span class="p">)</span>
<span class="c1"># Print the training and testing score</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Model: Random Forest Regressor"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Training Score: </span><span class="si">{</span><span class="n">training_score</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Testing Score: </span><span class="si">{</span><span class="n">testing_score</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Model: Random Forest Regressor
Training Score: 0.6535315248601461
Testing Score: 0.6465531713996515
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Residual plot</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
<span class="n">y_train_pred</span><span class="p">,</span>
<span class="n">y_train_pred</span> <span class="o">-</span> <span class="n">y_train</span><span class="p">,</span>
<span class="n">c</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">,</span>
<span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s1">'Training data'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
<span class="n">y_test_pred</span><span class="p">,</span> <span class="n">y_test_pred</span> <span class="o">-</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">'green'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'s'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Test data'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Predicted values'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Residuals'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper left'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">)</span> <span class="c1"># Add horizontal line at y=0</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Residual Plot'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [67]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># Predict on the test data</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">model_rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_imputed</span><span class="p">)</span>
<span class="c1"># Plot predicted vs actual values</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Actual vs Predicted'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">y_test</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">y_test</span><span class="o">.</span><span class="n">max</span><span class="p">()],</span> <span class="p">[</span><span class="n">y_test</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">y_test</span><span class="o">.</span><span class="n">max</span><span class="p">()],</span> <span class="s1">'k--'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Ideal Line'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Actual Values'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Predicted Values'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Random Forest: Actual vs Predicted'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<pre><ipython-input-67-3a08ba953780>:9: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string "k--" (-> color='k'). The keyword argument will take precedence.
plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2, color='red', label='Ideal Line')
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<p>The above code provided training a random forest regression model to predict median house prices in Melbourne.</p>
<ul>
<li>Defined a random forest regression model using RandomForestRegressor used to combine multiple decision trees to improve prediction accuracy and reduce overfitting.</li>
<li>By fitting the random forest model to the training data and assessed its performance on both the training and testing sets using the same as with the linear regression model.</li>
<li>The random forest model achieved a training score of 0.6535 and a testing score of 0.6465. This is significantly higher than the scores obtained with the linear regression model,stating that the random forest model is better suited for this dataset and task.</li>
<li>This indicated that using a random forest regression model led to a
significant improvement in the model's ability to predict median house prices compared to the linear regression model.</li>
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<p><strong>In Conclusion</strong></p>
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<p>Based on the Project objective to understand the fluctuations in employment levels within different ANZSIC industries impact housing prices in Melbourne's diverse neighborhoods, we can emphasize based on the Random Forest Regressor model and Linear Regression Model scores:</p>
<ul>
<li>By analyzing the datasets on employment trends and housing prices, gained insights into the relationship between these two factors. Through statistical analysis and visualization, and explored the fluctuations in employment levels across different industries correlate with changes in housing prices within the City of Melbourne.</li>
<li>Identified patterns, trends, and correlations between employment and housing markets which includes understanding of shifts in employment levels within specific industries influence housing demand, property prices, population demographics, and housing preferences.</li>
<li>The analysis conducted provides valuable insights for urban planners, policymakers, and stakeholders involved in economic development and urban planning within Melbourne. By understanding the correlations between employment and housing markets, decision-makers can make informed choices regarding housing affordability, workforce development, and infrastructure planning.</li>
<li>The implementation of machine learning algorithms, specifically the Random Forest Regression model, further enhances your understanding of the relationship between employment and housing prices. Achieving relatively high training and testing scores with the Random Forest model validates its effectiveness in predicting median house prices based on employment trends.</li>
<li>The Random Forest Regressor model achieved relatively high training and testing scores of approximately 0.65, indicating that it effectively captures the relationship between employment levels in various ANZSIC industries and housing prices.</li>
<li>The Linear Regression model demonstrates moderate performance, with training and testing scores indicating that it captures some of the variability in the data and the model provides some insight into the relationship between employment fluctuations and housing prices, its performance suggests that it may not fully capture the complexity of the relationship.</li>
<li>The Random Forest Regressor model outperforms the Linear Regression model significantly, with higher training and testing scores.Its performance indicates that it captures the complexities of the relationship between employment levels within ANZSIC industries and housing prices in Melbourne's neighborhoods.</li>
<li>The Random Forest model's ability to generalize to unseen data suggests that it provides a more accurate representation of the relationship, making it better suited for predictive modeling in this context.</li>
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<h2>Reference</h2>
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<p><a href="https://christianmartinezfinancialfox.medium.com/how-can-we-use-machine-learning-to-predict-the-sales-of-a-new-product-cf94462af8ff">https://christianmartinezfinancialfox.medium.com/how-can-we-use-machine-learning-to-predict-the-sales-of-a-new-product-cf94462af8ff</a></p>
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<p><a href="https://nthu-datalab.github.io/ml/labs/04-2_Regression/04-2_Regression.html">https://nthu-datalab.github.io/ml/labs/04-2_Regression/04-2_Regression.html</a></p>
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<p><a href="https://dooinnkim.medium.com/how-to-plot-predicted-vs-actual-graphs-and-residual-plots-dc4e5b3f304a#:~:text=A%20Predicted%20vs%20Actual%20plot,with%20a%20slope%20of%201">https://dooinnkim.medium.com/how-to-plot-predicted-vs-actual-graphs-and-residual-plots-dc4e5b3f304a#:~:text=A%20Predicted%20vs%20Actual%20plot,with%20a%20slope%20of%201</a>.</p>
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<p><a href="https://statisticsbyjim.com/graphs/scatterplots/">https://statisticsbyjim.com/graphs/scatterplots/</a></p>
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